Knowledge Engineering: a Learning and Application Guide

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St. Petersburg State University Graduate School of Management

T.A. Gavrilova, S.V. Zhukova

KNOWLEDGE ENGINEERING: a learning and application guide

St. Petersburg 2012

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Reviewers: Professor A.G. Medvedev, Doctor of Economics, Graduate School of Management SPbSU Professor A.V. Smirnov Doctor of Science, deputy director of SPII RAS Published in accordance with requirements of Curriculum Design and Development Committee, Graduate School of Management SPbSU

Gavrilova T.A., Zhukova S.V. Knowledge Engineering: learning and application guide / T. A. Gavrilova, S. V. Zhukova; Graduate School of Management SPbSU. — SPb.: Publishing Centre “Graduate School of Management”, 2012. — p. 133.

Knowledge Engineering is the discipline of mapping intellectual assets. Through this guide, students are introduced to the major practical issues of knowledge engineering techniques. Developing business information structuring skills are the key to successful knowledge representation and sharing in any organisation. Students are trained to use Mind Manager and CMap software in order to support understanding of highly multidisciplinary horizons of knowledge engineering. Applications of recent advances in information processing and cognitive science to management problems are introduced in a variety of interrelated exercises designed to form an e-portfolio. The design of an e-portfolio makes it possible to reveal the tradeoffs of visual knowledge modelling, invent and evaluate different alternative methods and solutions for better understanding, representation, sharing and transfer of knowledge. The guide is written to support “Knowledge Engineering” delivered to students of the “Master of International Management” graduate program.

© Graduate School of Management SPbSU, 2012

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Contents PREFACE ............................................................................................................................. 5  INTRODUCTION .................................................................................................................. 7  CHAPTER 1. CONCEPTUAL MODELLING ............................................................................... 8  1.1. INTENSIONAL AND EXTENSIONAL DEFINITIONS .............................................................................8   1.2. MINDMAPS .........................................................................................................................9   1.3. CONCEPT MAPS ..................................................................................................................11   1.4. FRAMES ............................................................................................................................13   CHAPTER 2. DECISION MODELLING ................................................................................... 14  2.1. DECISION TABLES ................................................................................................................14   2.2. DECISION TREE ...................................................................................................................16   2.3. CAUSE AND EFFECT DIAGRAM ...............................................................................................20   2.4. FLOWCHARTS .....................................................................................................................21   2.5. CAUSAL CHAINS ..................................................................................................................25   2.6. FUZZY KNOWLEDGE .............................................................................................................27   2.7. КNOWLEDGE ELICITATION AND STRUCTURING ...........................................................................28  CHAPTER 3. REPRESENTING KNOWLEDGE WITH ONTOLOGIES ........................................... 30  3.1. TYPES OF ONTOLOGIES .........................................................................................................32   3.2. ONTOLOGICAL ENGINEERING.................................................................................................34   CHAPTER 4. SELF‐TRAINING IN KNOWLEDGE ENGINEERING............................................... 35  4.1. ROADMAP OF IN‐CLASS ASSIGNMENTS .....................................................................................35   4.2. E‐PORTFOLIO DEVELOPMENT .................................................................................................36   4.3 PREPARING FOR A FINAL EXAM ................................................................................................40   APPENDIX A. COMPUTER SCIENCE HISTORY FACTS ............................................................ 41  APPENDIX B. ORCHESTRATING ONTOLOGIES..................................................................... 51  APPENDIX C. BUSINESS ENTERPRISE ONTOLOGIES............................................................. 75  APPENDIX D. INFORMATION MAPPING SOFTWARE........................................................... 91  APPENDIX E. COURSE SYLLABUS “KNOWLEDGE ENGINEERING” ......................................... 95  APPENDIX F. AN EXAMPLE OF AN E‐PORTFOLIO .............................................................. 105  CONCLUSION .................................................................................................................. 128  REFERENCES ................................................................................................................... 129 

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Preface This guide is intended to support students in understanding the basics of knowledge engineering and structuring in order to apply intelligent technologies to various subject domains (business, social, economic, educational, humanities, etc.). The discipline of knowledge engineering gives students insight and experience in the key issues of data and knowledge processing in various companies. Via in-class discussion sessions and training, students reveal the tradeoffs of visual knowledge modelling, invent and evaluate different alternative methods and solutions for better representation and understanding, sharing and transfer of knowledge. This book is targeted at managers of different levels, involved in any kind of knowledge work. The course’s goals are focused on using the results of multidisciplinary research in knowledge engineering, data structuring and cognitive science in information processing and modern management. The hands-on character of this course fosters learning by doing, case studies, games and discussions. Practice is targeted at e-doodling with the Mind Manager and Cmap software tools. A good deal of the course focuses on auto-reflection and auto-formalisation of knowledge, training analytical and communicative abilities, discovery, creativity, systemic analysis of new perspectives, synthesis of evidence from disparate sources of information, and gaining new insights in this fascinating emerging field. Since knowledge engineering is the discipline of mapping intellectual assets, it introduces a lot of visualisation techniques to represent data and knowledge by means of business information structuring. Special software (mind mapping and concept mapping) makes it possible to amplify the positive effects of knowledge acquisition and save time for managers at the documentation stage of knowledge work. The assignments designed to form an e-portfolio examine a number of related topics fully described in the course syllabus, such as: • •

system analysis and its applications; the relationship among, and roles of, data, information, and knowledge for different applications, including marketing and management, and various approaches needed to ensure their effective implementation and deployment;

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•

the characteristics of the theoretical and methodological topics of knowledge acquisition, including the principles, visual methods, issues, and programs; • defining and identifying cognitive aspects for business knowledge modelling and visual representation (mind mapping and concept mapping techniques); • developing different business diagrams, such as decision trees, decision tables, causal chains, etc. The examples in the appendices are partially comprised from real students” portfolio and may have some mistakes and errors.

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Introduction The need to exchange and reuse knowledge became a global problem for the scientific and research community with the exponential growth of the Internet. Knowledge engineering is not only a science that studies knowledge processing (elicitation, structuring and formalisation) for intelligent (or knowledge-based) systems development, but also contains techniques crucial for each and every modern company that considers knowledge a key intellectual asset. The domain of knowledge engineering has expanded greatly in recent years and now includes the elicitation (or acquisition), collection, analysis, modelling and validation of knowledge for knowledge management projects. One issue that presents particular interest is the symbolic representation of knowledge. Knowledge engineering principles. Since the mid-1980s, knowledge engineers have developed a number of principles, methods and tools that have considerably improved the process of knowledge acquisition. Some of the key principles may be summarised as follows: • knowledge engineers acknowledge that there are different types of knowledge, and that the right approach and technique should be used for the knowledge required; • knowledge engineers acknowledge that there are different types of experts and expertise, and that methods should be chosen appropriately; • knowledge engineers acknowledge that there are different ways of representing knowledge, which can aid the acquisition, validation and re-use of knowledge; • knowledge engineers acknowledge that there are different ways of using knowledge, and so the acquisition process can be guided by the goals of the project; • knowledge engineers use structured methods to increase the efficiency of the acquisition process. Issues in knowledge acquisition. Some of the essential issues in knowledge acquisition are formulated as follows: experts are individuals and the owners of the

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Chapter 1. Conceptual modelling

knowledge in their heads; experts have both tacit and explicit knowledge; experts are always busy and not interested in sharing knowledge; knowledge has a very specific life cycle. Requirements for knowledge acquisition techniques. Because of knowledge acquisition issues, special techniques are required: taking experts off the job for short time periods, allowing non-experts to understand the knowledge involved, focusing on the essential knowledge; capturing tacit knowledge, allowing knowledge from different experts to be collated, allowing knowledge to be validated and maintained.

Chapter 1. Conceptual modelling Knowledge is a high level concept of abstraction that encompasses a lot of interrelated facts from human experience. The formalisation of knowledge in clear hierarchies of concepts, terminology and explicit solutions has to overcome complicated issues of human intuition and cognition. One of the most successful ways to begin the extraction and articulation of knowledge is the visualisation of concepts and the creation of visual models of knowledge. Visualising techniques make it possible to focus on the so-called WHAT-knowledge aspects, to arrange and clarify relationships between concepts, thoughts and ideas, to observe the borders of concepts’ meanings within the domain under consideration, when a particular management problem is on the agenda.

1.1. Intensional and extensional definitions A rather large and especially useful portion of our active vocabulary is taken up by general terms, words or phrases that stand for whole groups of individual things sharing a common attribute. But there are two distinct ways of thinking about the meaning of any such term. The extensional of a general term is just the collection of individual things to which it is correctly applied. Thus, the extension of the word "chair" includes every chair that is (or ever has been or ever will be) in the world. The intension of a general term, on the other hand, is the set of features which are shared by everything to which it applies. Thus, the intensional of the word "chair" is (something like) "a piece of furniture designed to be sat upon by one person at a time." You can find another example of intensional/extensional definitions in Fig. 1.

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Intensional

Pets that barks

Extensional

Dog

Scotch-Terrier

Labrador

Collie

Fig. 1.1. Intensional and extensional definition of the term “Dog”

1.2. Mind maps The area of application for mind maps is very broad, since this type of diagramming serves to capture thoughts and ideas on paper. According to the evidence produced by the neurosciences, the human brain is a powerful biological computer with parallel nonlinear processing of electro-chemical signals. The parallel nature of thinking is reflected in the process of mind mapping that starts with putting the main concept or problem in the centre of the picture. All other items related to the key concept or problem find their place on the radially arranged branches starting from the centre of a map. In accordance with the peculiarities of visual perception, the more important one of the aspects of the key word is, the more distant it is from the centre. It is advisable to limit the number of branches to nine, as was shown by Miller in 1956; a human being has a limited capacity for processing information and cannot handle more than nine objects of attention simultaneously. The depth of knowledge on the subject of the key concept (word, problem) is explored by means of hierarchical representation of more and more detailed issues placed on the sub branches of the map. Mind maps are used to clarify one’s vision in a form that is easily transferred between managers working for any organisation. The orchestration of branches depends greatly on the semantic and logical connections between portions of information. Mind maps are used to structure and visualise ideas, which is one of the most important stages of any decision-making and problem-solving process. The origins of mind mapping date back to ancient papers by Porphyry of Tyros, Aristotle, and Llull.

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The modern technique of mind mapping was reinvented recently by Tony Buzan. The main idea is to direct managers’ attention away from their habitual right-left and topdown processing of the pictures’ content and towards nonlinear perception of the whole map at one glance, including all the details. As the brain functions in a nonlinear way, it is proper to use curves instead of straight lines to mimic the nature of thinking, in order to increase the success of note-taking. This tool is highly advantageous when used in brainstorming sessions, when people are expected to present their raw thoughts within strict time limits. There are some useful tips to develop effective mind maps. These tips are based on advances in neurobiology and cognitive sciences and can be summarised as follows: • Begin with the key concept and place it in the centre. • Use different colours (no more than three) to emphasise the related items. • Support the curves with self-explanatory pictograms and symbols. • Represent the importance of the item by means of the hierarchy level of the branch in the way that the font size of the text placed on the curve and thickness of the curve decrease as the level increases. • Place items of the same scale of abstraction on the same level. • Limit the text above a branch to several very concise and appropriate words to articulate the item. • Connect the branches with the central concept. • Make the lines the same length as the word/image. • Use a mind map to show the associations.

Fig. 1.2. Example of a Mind map ( by Free Mind)

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1.3. Concept maps Concept maps are graphical tools for organising and representing knowledge. They include concepts, usually enclosed in circles or boxes of some sort, with the relationships between the concepts indicated with a connecting line linking two concepts. The words on the lines are referred to as linking words or linking phrases, and specify the relationship between the two concepts. The label for most concepts is a word, normally a noun. Propositions are statements about some object or event in the universe, either naturally occurring or artificially constructed. Propositions contain two or more concepts connected using linking words or phrases to form a meaningful statement. Sometimes these are called semantic units, or units of meaning. Some examples of concept maps are given in Fig. 1.3, Fig. 1.4. Another characteristic of concept maps is that the concepts are represented in a hierarchical fashion, with the most inclusive, most general concepts at the top of the map and the more specific, less general concepts arranged hierarchically below. The hierarchical structure for a particular domain of knowledge also depends on the context in which that knowledge is being applied or considered.

Fig. 1.3. Example of a concept map (Cmap tool)

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There are several types of relations in concept maps: Œ Hierarchical (A-Kind-Of, Is-A) Œ Causal (if- then) Œ Quantitative (more than, equal…) Œ Functional (runs, eats, is…) Œ Spatial (on, behind, inside…) Œ Temporal (after, before, until…) Œ Attribute (colour, weight…) Œ Value (red, heavy… ) Œ Structural (has-part)

Fig. 1.4. Example of a concept map “Seasons” (Cmap tool)

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1.4. Frames The concept of FRAMES was proposed by Marvin Minsky in 1972. Frames are used as an abstract structure for the representation of stereotypes of complex objects, process, events, and scenarios. Cognitive psychologists have confirmed the fact that the human brain uses frames to store knowledge. Frames could also be used to model rules and stereotypes of behaviour. A frame consists of various (list of) attributes called slots. The main and obligatory slot is called AKO (A-kind-of). Example of a frame “Cottage” SLOT1. AKO: House SLOT2. Quantity of floors: 1 to 3 SLOT3. Numbers of rooms: ≤6 SLOT4. Colour: SLOT5. Price: SLOT6. Address: SLOT7. Purpose: To develop a frame one should think first about the most specific properties of an object that distinguish it from similar concepts.

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Chapter 2. Decision modelling After exploring the problem domain and specifying the direction of further improvement by means of visualisation and documentation tools, it is time to identify the causes of the currently unsatisfactory situation and define the business logic of reaching a solution to the problem. There are several management tools that help to put results of WHAT-knowledge analysis into HOW-knowledge practice.

2.1. Decision tables A decision table is a tabular form that presents a set of conditions and their corresponding actions. For corporate management they are called “business rules”. The structuring algorithm looks like this: R} { X } ⎯{⎯→ {Y }

Define goals, outputs or decisions {Y} (Fig. 2.1). Create the glossary of input factors or facts (conceptual structure) {X} (Fig 2.2). Make up the functional structure or rules{R} - decision table or reasoning model (Fig.2.3)

Y1 Go {Y} Recommendations

Y2 Wait (Stop)

Fig. 2.1. Example of recommendations (Y)

Fig.2.2. Example of a conceptual structure (X)

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x1

x2

x3

Xn

y

P

Colour of traffic lights

Policeman

Cars

…

Choice

Confidence

stop go go

0.9 0.7 0.8

Red or yellow Green Yellow

Far

far

Fig. 2.3. Example of a functional structure (R) in the form of the decision table

Another example is described below. The tools that were considered above make it possible to formalise knowledge in order to understand and communicate more efficiently. The process of formalisation in this guide consists of two primary stages: • knowledge elicitation and then • visual structuring or mapping. One is very lucky if all the information needed is just stored in his/her mind, but the situation is usually rather different; one has only part of the picture and a lot of people should be approached and interviewed to gain the missing knowledge. In other words, you should begin by working as an expert or acquire knowledge from an expert, and then work as a business analyst to choose the proper tool to visualise the knowledge. For example the output of gaining knowledge about a bank loan strategy can result in an expert’s knowledge field conceptual structure (Fig. 2.4).

Influences

Customer size • High • Medium • Low

Enterprise type • State • Commercial

Position

• • • •

CEO Top manager Manager Employee

Length of service

Age

• > 5 years

• < 30 • 30-50 • >50

• < 5 years

Fig. 2. 4. Expert’s knowledge field on bank loan strategy

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After clarification of the knowledge field, a business analyst may develop a decision table to describe the loan strategy in more formalised way (Fig. 2.5) that is a more proper form to communicate with other managers and programmers. Elicitation of knowledge from an expert is a time-consuming and labour- intensive process that should be carefully planned and documented. The art of interviewing is a multifaceted one. The most important aspect from a knowledge formalisation point of view are developing questionnaires and transcripts. A transcript delivers raw data to the business analyst, who can then use it to create business rules, which are then used in the development of a decision support system. The following example shows the transfer of raw data into the decision system with business rules and conceptual structures in detail. Customer size

Enterprise type

Decision

Level of confidence

High

Trust

0.7

Low

Reject

0.6

Medium

Request additional information

0.5

30– 50

Trust

0.7

< 30

Trust

0.8

Position

Medium

State

Top manager

Medium

Commercial

CEO

Length of service

>5 years

Age

Fig. 2.5. Business analyst’s decision table (functional structure)

2.2. Decision tree A decision tree (or tree diagram) is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences (alternatives), including the outcomes of random events, resource costs, and utility. Decision trees are commonly used in operations research, specifically in decision analysis, to help identify the strategy most likely to facilitate reaching a particular goal. Decision Trees are useful tools for helping you to choose between several courses of action. They provide a highly effective structure within which you can explore options, and investigate the possible outcomes of choosing those options. They also help you to form a balanced picture of the risks and rewards associated with each possible course of action. This makes them particularly useful for choosing between

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different strategies, projects or investment opportunities, particularly when your resources are limited.

X1 — temperature X138.5°C

37°C ≤X1≤38.5°C X2 — presence/absence of a cough absent influenza

X3 — throat colour

present pneumonia

normal common cold

red tonsillitis

Fig.2.6. Diagnosing a patient by means of a decision tree

A set of logical statements about the values of characteristics corresponds to decision trees. Each statement is obtained by passing the way from root to leaf. So, for example, for the tree represented in Fig. 2.6 the following list of statements corresponds to the RULE-BASED MODEL: If X1 < 37 , Y="is healthy". If X1 belongs to the interval [37,38.5] and X3="there is no reddening of throat", then Y="to catch cold"; If X1 belongs to the interval [37,38.5] and X3="there is reddening of the throat", then Y="tonsillitis"; If X1 > 38.5 and X2="absent", then Y="influenza"; If X1 > 38.5 and X2="present", then Y="pneumonia"; An example of applying a decision tree to the analysis of alternatives about what to do for Christmas vacation is represented by Fig. 2.7 .

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Chapter 2. Decision modelling

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Stay at home

Relax

1: “A good way to reduce stress, but it may get boring in the long run”

Go shopping

2: “I’ll get the Christmas shopping done, but I may get very tired”

Go to the Christmas Market

3. “The fun and pretty market will give me some real Christmas Spirit, but it will be very cold there”

Christmas Vacation

Go traveling

19

Somewhere with snow

France

4. “Has very nice skiing conditions, but is very far away”

Austria

5: “Never skied in Austria, so it’s a new experience, but I don’t speak the language

Switzerland

6. “It’s a very beautiful country with lots of nice hikes available”

Cuba

7: “Very cheap, but I don’t speak the language”

Thailand

8. “Everyone speaks English, it’s very cheap, but robberies are common”

Mauritius

9: “Everyone speaks English”, it’s a small island — there’s only so match to see

Somewhere That’s warm

Visit to my parents

Go for a lot of walks

Relax

10. “It will be nice to see the old neighbourhood, but it may get tiresome" 11. “A good way to reduce stress, but it may get boring in the long run”

Fig. 2.7. An Exploration of Christmas vacation alternatives by means of a decision table

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2.3. Cause-and-Effect Diagram The purpose of a Cause and Effect diagram is to organise and graphically represent the causes of a particular problem (Fig. 2.8–2.9). In a Cause-and-Effect diagram, also referred to as an Ishikawa diagram or Fishbone diagram, the problem is put at the “head” of the fish. The major fish bones are typically major drivers. From these major drivers, specific drivers that are causing the problem are listed. Besides the major drivers some sublevels and sub-sublevels can also be added as additional branches for each of these branches. An Effect can be represented as a Problem, Objective, or Goal. The major drivers are the categories of factors that influence the effect being studied.

Fig.2.8. Cause & Effect diagram template (MS Visio)

One can identify the following benefits of using a Cause & Effect diagrams. • Helps to determine root causes. • Encourages group participation. • Uses an orderly, easy-to-read format. • Indicates possible causes of variation. • Increases knowledge of processes. • Identifies areas where there is a lack of data. To draw a cause and effect diagram in MS Visio, you need to use the Business Process type of diagram, and then specify the C&E type.

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Fig. 2.9. An example of a Cause & Effect diagram (by Ilia Beloly)

2.4. Flowcharts A flowchart is a graphic representation of a sequence of steps that define some business process. The flowchart tool is widely used when improving business processes is on the agenda. When all participants share a common vision of how the work is currently done in the organisation, it is much easier to identify bottlenecks and decide upon the changes that should be made to improve the process in order to satisfy customer needs more effectively. The main aim of a flowchart is to give a snapshot of how things really stand and the more relative and detailed the flowchart is (application of flowchart symbols) the more useful it would be in communications. The set of symbols that may occur in a flowchart one can see in Fig. 2.10.

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Chapter 2. Decision modelling

Fig. 2.10. Flowchart symbols (adapted from Flow Chart Symbols Cheat Sheet by Nicholas Hebb)

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Each and every flowchart has several required symbols (Fig. 2.11): • Start/End symbols (terminators) to define the boundaries of the process; • Process blocks to represent the steps of the action; • Flows (arrows) to show the direction of the steps. Start

Step 1 Boundaries Step 2

End

Fig. 2.11. Required symbols for any flowchart

There are several hints that can help to overcome potential misunderstanding while creating and, more importantly, reading flowcharts. To visualise the alternatives related to a particular decision, decision blocks are applied. It is important to understand that binary logic underlies the decision block (Fig. 2.12a). That means that it can have only one input and two outputs, labelled with Yes and No (and not three variants). The intersection of different branches can be very complicated. To clarify the flow of activities it is better to mark the cross of lines as it is shown in Fig. 2.12b. When flowchart takes several pages its different parts can be joined into one by means of connector block. First connector is put on the previous page (with some unique number or letter) and the second similar connector is put on the next page (in Fig. 2.12с connector with identifier A is put at the end of page 1 and appears on page 2 to mark the continuation of the flowchart). A flowchart should have only one End (termination) block. If several branches lead to the same result, these branches lead to the same termination block.

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Fig. 2.12. Hints on flowchart development: a) – special mark indicating that a line is being crossed; b) Yes and No alternatives for a decision block; c) – use of a connector to place the flowchart on different pages

Fig. 2.13. Example of a Flowchart in EDraw

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2.5. Causal chains A causal chain is an ordered sequence of events which shows that one event in the chain causes the next. While Cause & Effect diagrams make it possible to explore one problem, causal chains help to track a variety of problems and consequences. An example of part of a causal analysis framework used to identify the causes of child malnutrition is represented in Fig. 2.14. Another example describing the possible reasons and consequences of failing exams is presented in Fig. 2.15.

Fig. 2.14. Part of Causal chain describing only the possible reasons for child malnutrition, without outcomes (source: Bessler et al. “Sanctions Assessment Handbook”, 2004)

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Fig. 2.15. Example of a causal chain, “Failing an Exam” (by Julia Boyko)

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2.6. Fuzzy knowledge A new branch of logic called fuzzy logic was proposed in 1970 by Lotfi Zadeh. It was targeted at processing quantitative variables and not qualitative variables. This was done based on the assumption that people use the quantitative attributes of reasoning. For example, “age” is a linguistic variable: Age = {child, young, mature, adult, old}

FS :=

∑ xi⋅μi i

where µ is the coefficient of uncertainty (confidence level) or membership function, it shows the level of belonging to the fuzzy set (FS). In order to define fuzzy set, we require a basic discrete scale (in years, or kilograms, or meters, etc.). This formula describes FS “Young age”:

young

FS

:=

1 0.6

+

5 0.7

+

10 0.8

+

15 1

+

20 0.9

+

25 0.8

+

30 0.6

To the mind of the expert that that developed the formula a young man means a person near his 15 (membership function is 1, 100%). Fuzzy set also may be presented as a plot. For example fuzzy set that corresponds to “Child age” can be visualised at it is shown in Fig. 2.16. The younger the little person is, the more confident we are about calling him/her a child.

m(х)1 0,8 0,6 0,4 0,2 0 0

1

2

3

4

5

6

7

Fig. 2.16. Example of a fuzzy set, “Child’s age”

8

9

10 Age

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2.7. Knowledge Elicitation and Structuring Knowledge elicitation from an expert is a time-consuming and labour-intensive process that should be carefully planned and documented. The art of interviewing is a multifaceted one. The main thing from a knowledge formalisation point of view is the development of questionnaires and transcripts. A transcript delivers the raw data to a business analyst, who can use it to create the business rules subsequently used to develop a decision support system. The following example shows the transfer of raw data into a decision system with business rules and conceptual structures in detail. Protocol of expert knowledge elicitation Topic: Travelling in Corsica Business analyst: Marius Giwer Interviewee (Expert): Thomas Elicitation-Technique: Interview Date: 17.11.08 Duration: 35 min. Questions: Total: 15 Open questions: 11 Multiple Choice: 4 Personal: 3 Impersonal: 12 Direct: 12 Indirect: 3 Completed from: 1) Would you describe Corsica as a typical part of France? Yes No 2) What are the differences? Isolated; Different History; Behaviour; Pride. 3) How many times have you been there? 0 1-3 4-6 7-10 >10 4) Mark the groups for whom Corsica might be an attractive travel destination: Teenager Couples Families Seniors Individuals DINKs 5) Is it an expensive travel destination? Yes No 6) Describe the landscape: Rocky little beaches and raw sand (south), long beaches (north), dry climate. 7) What do tourists like most on Corsica? Beaches, Porto Vecchio, Bonifacio 8) Is it good for individual travel? No, too dangerous, the environment is quite pristine and the climate changes harshly., Several tourists die every year, because they underestimate the climate. 9) Is it good for adventure travelling? Its very interesting, because nature is pristine and varied there.

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10) Which sports are available on Corsica? Skiing in winter (just small places), climbing, hiking, surfing, kite-surfing, swimming, snorkelling, water-skiing. 11) List some cultural attractions: Few museums, Roman ruins (antiquities), beautiful countryside. 12) Which place would you recommend to a friend? Bonifacio. 13) What is the best place to stay? If money is not a problem, the best option is to stay in a villa, or a house. 14) Where do most of the tourists there come from? France and Italy, there are also some Germans. 15) What else could you mention about Corsica? You can reach it from the French coast with your boat, it’s a wonderful trip.

Fig. 2.17. Conceptual structure & Decision table

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Chapter 3. Representing knowledge with ontologies An ontology is an explicit specification of a simplified view of the world that we want to represent. In this guide, we understand the term “ontology” to mean "Information Ontology" but not philosophical ontology. Ontologies may help the research community to generalise their shared notions. Ontology is a set of distinctions we make in understanding and viewing the world. There are numerous definitions of this seminal term (Neche et al ,1991; Gruber, 1993; Guarino et al, 1995; Gomez-Peres, 1999). Together, these definitions clarify the ontological approach to knowledge structuring while leaving enough room for openended, creative thinking. So, for example, ontological engineering can provide a clear representation of a course structure, main concepts, approaches, terms and their interrelationships. It originated in the field of knowledge engineering (Boose, 1990; Wielinga, Schreiber, Breuker, 1992), then it was transferred to knowledge management (Fensel, 2001). Ontologies are useful structuring tools, in that they provide an organising axis along which every student can mentally mark his vision in the information hyperspace of domain knowledge. Frequently, it is impossible to express the information as a single ontology. Accordingly, subject knowledge storage provides for a set of related ontologies. Some problems may occur when moving from one ontological space to another, but constructing meta-ontologies may help to resolve these problems. Since we are speaking about the pre-design stage of creating light-weight ontologies (without formalising it into Web Ontology Language or other language), it may be helpful to use any available graphical editors may be helpful. These editors work as powerful assistants. You may get the best results by using mind mapping and concept mapping tools. There are many popular tools that have been developed to create Mind Maps. These tools are limited, in the sense that they typically cannot produce any other type of graph structure, and do not allow for explicit linking terms within or across branches. Mind Maps are often very colourful, so the best commercial software has sophisticated colour and imaging options. These tools are very handy, simple and clear, so their features make them more user-friendly and produce more impressive representations. Some of these tools have become fairly sophisticated in terms of linking to Internet resources, adding notes, and support for collaborative use. The most widely used and developed versions include: • Mind Manager from MindJet http://www.mindjet.com/index.shtml. • Visual Mind http://www.visual-mind.com.

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VisiMap by CoCo Systems http://www.coco.co.uk. http://www.coco.co.uk./prodvm.html. Mind Mapper from SimTech USA http://www.mindmapper.com. Concept Draw has a Mind Mapping product, as well as general drawing and diagramming products that integrate with Visio. More information is available at http://www.conceptdraw.com/en/products/CDPMindMap.

An extended list of software tools one can find in Appendix D. But any effective computer program for ontological engineering should perform the functions described for structuring the stages of a subject domain. Accordingly, it should correspond to the phenomenological nature of the knowledge elicitation involved, using different appropriate algorithms. This program must support the knowledge engineer by incorporating "rules of the game" that are clear, transparent, and functional. Ideally, the knowledge engineer should be able to tailor the program to his or her specific requirements. Concerning this, each analytical stage may be represented visually and accurately modelling the knowledge domain, an element that has already been realised in some commercial expert system shells.

Fig. 3.1. Summarising the ontology classifications in a mind-map

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Different ontology classifications are illustrated in Fig. 3.1 in the form of a mind map. Mind-mapping (Buzan, 2005) and concept mapping (Novak & Canas, 2006) are now widely used for visualising ontologies at the design stage.

3.1. Types of ontologies Relations describe the interactions between concepts or a concept's properties. Relations also fall into several broad kinds (Gavrilova, Koshy, 2004): Taxonomies organise concepts into sub- super-concept tree structures by using AKO-relation (A-kind-of). Partonomies use partitive relationships (has_ part) describe concepts that are parts of other concepts. Attributive structures describe the main attributes or features of the concept. Genealogies use links such as “Father of” or “Predecessor of.” Examples of ontologies are presented in Fig. 3.2–3.5.

Fig. 3.2. Taxonomy example for ball games

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Fig. 3.3. Attributive structure of a room (by Yaroslav Pavlov)

Fig. 3.4. Partonomy of Master Thesis (by Yaroslav Pavlov)

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Fig. 3.5. Genealogy of operating systems (by Yaroslav Pavlov)

3.2. Ontological Engineering The use of ontology engineering also appears to amplify the benefits associated with a structured approach. Major experts have stated that: • Ontology is interdisciplinary. • The demand for ontologists is expected to rise considerably, but employers cannot easily recognise qualified ontologists. • There is a large gap between educational needs and the availability of knowledge engineering education. • Training opportunities are in high demand among working professional managers. • Available training opportunities for professionals do not meet their needs. • Important subjects are absent from existing curricula. You can find more information on ontological engineering in Appendix B and Appendix C.

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Chapter 4. Self-training in knowledge engineering To prepare for an exam on knowledge engineering, one should attend computer labs and complete this list of in-class assignments rather than individually develop an e-portfolio that consists of the whole set of tools considered above. Below you can find lists of in-class assignments and obligatory tasks for an e-portfolio.

4.1. Roadmap of in-class assignments The roadmap is represented in Table. 4.1. Table 4.1. In-class lab-assignments Assignment

Hints on completion

Make INTENSIONAL AND EXTENSIONAL definitions of the concept “Book”

Use MS Word. Choose any concept you like except those from this guide

Make a visual draft of a history of computer science

Use MS Visio. You can find the main facts on computer science history in Appendix A

Draw a mind map of UNIVERSITY

Use FreeMind tool. Watch and listen to following video podcasts: Tony Buzan. about Mind mapping. http://www.youtube.com/watch?v=MlabrWv25q Q&feature=related. How to make a mind map Version 1 http://www.youtube.com/watch?v=v8_H42Z9w xA&feature=related. How to make a mind map Version 2 http://www.youtube.com/watch?v=0UCXalYco ko&feature=related Look through mind map examples on the web http://mappio.com Start FreeMind software. Look through Documentation in the part named “Demonstration of some features”

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Assignment

Hints on completion

Design your visual CV in the form of a mind map

Use the FreeMind tool. Use the Cmap tool. The sentence “The EVTEK Company will implement an expensive CRM system into routine daily performance by the end of 2008”

Draw a Concept Map Entitled “Shopping”

Use the C-Map tool

Write down the RuleBased Model

Use MS Word. Decision issue: “What gift should I bring to a birthday party?”

Create a concept map

Use C-map tool. Concept “VACATIONS”

Find 3 examples of expert systems in business on the web

Document your answers in a MS Word table with three columns Title of expert system/Short description of expert system/ Link to the web resource

Create a Decision Table

Use MS word. Decision issue “What clothes should I wear when going out?”

Create a Decision Tree

Use MS word. Decision issue“ Setting up a birthday party”

Work out the FRAME for a concept

Use MS Word. Concept of a “Newspaper”

Extract knowledge from the Use C-map and Wordle.net tools. Analyze text from given text Appendix B and Appendix C Create an ontology of a concept

Use FreeMind. Concept of “Management”

4.2. E-portfolio development An E-portfolio should be developed individually. By developing an Eportfolio, a student demonstrates his skills in the application of modern software tools to knowledge engineering. For the convenience of students and instructors, the portfolio is divided into two parts. First part containing assignment 1-10 should be completed a week before by the midterm day. Second part containing assignment 11–20

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should be completed a week before the last class. The variants of assignments are grouped in Tables 4.2–4.3. The first column in these tables corresponds to the first letter in students’ family name and the variables in the headers of other columns correspond to different assignments. On the intersections of a row and a column, one can find his variant of variable’s value. Example of e-portfolio assignments: Part A1. 1. Create visual scheme of any SYSTEM (shop, company, movie, etc) 2. Make INTENSIONAL AND EXTENSIONAL definitions of a concept IE 3. Present the text compression in a form of the word cloud made by Wordle.net. 4. Draw A MIND MAP “My visual resume’ or “my world”. 5. Draw A MIND MAP of a concept IE. 6. Create A CONCEPT MAP for a sentence CM. 7. Create A CONCEPT MAP for “Management”. 8. Work out the FRAME for a concept F. 9. Create a DECISION TABLE for DTA. 10. Create a DECISION TREE for DTR. Part A2. 1. Create a CAUSE-AND-EFFECT DIAGRAM of CE. 2. Make a FLOWCHART of P process. 3. Make a CAUSAL CHAIN “Being Late to the Train”. 4. Describe the linguistic variable LV as a group of fuzzy sets, and de John Brown goes to a short business trip to Moscow by train on May with his younger colleague Nick Adams”. Describe one set us-ing the basic scale. 5. 40 properties of PR and its conceptual structure. 6. Extract knowledge from the given text. 7. Conduct INTERVIEW on the chosen domain and type down the protocol (work in pairs). 8. Present the conceptual structure of the domain DTE and decision table 9. Create a set of ONTOLOGIES (taxonomy IE and partonomy F). 10. Create a set of ontologies (attributive structure F and genealogy G).

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Table 4.2. E-portfolio variants for assignments from the first part of e-portfolio IE

CM

DTA

A

Footwear

B

Clothes

Purchasing a car

C

Car

D

Transport

E

Furniture

John Brown goes on a short business trip to Moscow by train, in May, with his younger colleague Nick Adams

F

Dish

G

Sport

Welcoming guests

H

Hobby

I

Curtain

G

Bird

K

Fish

Tim BernersLee is a “father“ of the WWW and an outstanding computer scientist of the XX century

L

Animal

Choosing a dog

M

Bag

N

Window

O

Gift

P

Painting

Q

Floor

Old computers are big, heavy, and unreliable electronic devices that are not easy to replace with modern gadgets

R

Wall

S

Door

Curing the flu

T

Wine

U

Glass

V

Flower

W

Tree

X

Student

Senior accountant Bob Shultz hides old reports on the black wooden bookshelf in the corner of room 305

YZ

Hero

F Book Boat

CE Nuclear catastrophe

Dressing gown

DTE How to apply to an university

Chair Paper Clock

Traffic Jam

How to set up a birthday party

Computer Bird Fish Desert Printer Mouse

Health problems

Bank River Lake Sky Sun

Personal development

Beach Aircraft Bus Victim Atom

How to write an essay

How to prepare a term paper

Vacation

Deed

How to buy a computer

Preventing a car breakdown

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Table 4.3. E-portfolio variants for assignments from the second part of e-portfolio IE A

Footwear

B

Clothes

C

Car

D

Transportation

E

Furniture

F

Dish

G

Sport

H

Hobby

I

Curtain

G

Bird

K

Fish

L

Animal

M

Bag

N

Window

O

Gift

P

Painting

Q

Floor

R

Wall

S

Door

T

Wine

U

Glass

V

Flower

W

Tree

X

Student

YZ

Hero

P Fishing

LV A person’s height

PR Tree

G Sciences

Species Studying

A person’s weight

Flower

Reading

The price of a gift

Plate

Computers Vehicles

Cup

Breakfast preparation

The size of a house

Operating systems

Sheet of paper

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4.3 Preparing for a final exam Variant 1 of the final test 1) Write down a frame of the concept of a “income” 2) Make a fuzzy description of the concept of “The price of a notebook” 3) Create ONTOLOGY (TAXONOMY) for the concept of “customer” 4) Flowchart of the process “booking of an airplane flight” 5) Concept map of “master’s thesis” 6) Extract knowledge from the text

Variant 2 of the final test 1) Write down a frame of the concept of a “passenger” 2) Make a fuzzy description of the concept of a “Student’s scholarship” 3) Decision table for the process “Planning Moscow-Helsinki travel” (choosing a type of transportation) 4) Ontology for the concept of a “company” as an attributive structure 5) Extract knowledge from the given text in the form of a concept map

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Appendix A. Computer science history facts

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Appendix A. Computer science history facts

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Appendix A. Computer science history facts

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Appendix A. Computer science history facts

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Appendix A. Computer science history facts

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Appendix A. Computer science history facts

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Appendix B. Orchestrating ontologies Gavrilova T. Orchestrating Ontologies for Courseware Design // in Affective, Interactive and Cognitive Methods for E-Learning Design: Creating an Optimal Education Experience (Eds. by A. Tzanavari & N. Tsapatsoulis), IGI Global, USA, 2010. – pp. 155-172. Abstract This chapter presents an approach aimed at creating teaching strategies for elearning based on the principles of ontological engineering and cognitive psychology. The proposed framework is important for many reasons. It is targeted at the development of methodologies and related technologies that can scaffold the process of knowledge structuring and orchestrating teaching ontologies for courseware design. The orchestrating procedure is the kernel of ontology development. Ontologies that describe the main concepts of the example domains are used for both teaching and assessment techniques. The main emphasis is placed on using visual techniques of mind-mapping and concept mapping as a powerful learning tool. Cognitive bias and some results of Gestalt psychology are highlighted as a general guideline. The ideas of balance, clarity, and beauty are applied to the procedures for orchestrating ontology. These examples are taken mainly from a course on C-programming, and the foundations of intelligent systems development. Key words: knowledge engineering, ontologies, visual courseware design.

Introduction During the last decade, visual knowledge representation has become one of the key considerations in e-learning methodology and it is heavily associated with ontology design and development. Furthermore, so-called teaching and learning ontologies have arguably begun to play a central role in courseware content. These ontologies, which are built on the conceptual skeleton of the teaching domain, might serve various purposes, such as better understanding, knowledge sharing, collaborative learning, problem solving, seeking advice, or developing skills by learning from peers. Recently, ontological engineering perspective has attracted interest in the domain of computer-aided learning and cognitive psychology involving the study of the structure and patterns of knowledge. These studies rely heavily on theory and tools from knowledge engineering analysis, which already has a longstanding tradition in the knowledge-based systems domain (Mizoguchi & Bordeau, 2007). The tools and tech-

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niques developed in this domain can be applied fruitfully to the field of learning structuring and design (Schreiber, 2000; Knight, Gašević & Richards, 2006), SemanticWeb applications (Davies et al., 2002). The idea of using ontologies and visual structuring in educational e-learning was discussed in many works (Davies, 20008; Gavrilova et al., 2003) and now several software tools are being implemented. These techniques can also be used as assessment tools also. Ontological engineering can also be used as an effective research instrument to study how the structure and patterns of the knowledge of a particular domain are related to other course content, such as hands-on tasks, quizzes, exercises, and slideshows or data repositories. Much of the research so far has focused on a limited number of formal representations that are typically easy to develop, while cognitive and methodological issues are rather underestimated. Furthermore, the categorisation and laddering as the creative synthesising activities also did not receive much attention in e-learning, while they proved their importance in socio-technical and management research. Regardless of how ontological engineering is used, in all cases it is necessary to analyze the design procedure. This is typically done using interviews with the students and teachers, which is a labour-intensive task. The ontologies described here were designed and orchestrated for courses on knowledge engineering delivered by the author in face-to-face and e-learning formats at the Graduate School of Management at SaintPetersburg State University, the School of Computer Science at St. Petersburg State Polytechnic University (both located in Russia) and the University of Milan (Italy), the ontologies for the C-programming course were developed for Dr. Peter Brusilovsky’s course at the School of Information Sciences and Libraries at University of Pittsburgh (USA), along with his PhD students. This chapter traces the cognitive foundations of educational design, using the methods of structured ontological engineering. The purpose of this methodology is to provide teachers and learners with distinct recommendations in ontology design and orchestration for better knowledge transfer and sharing.

Background The idea of using visual structuring of information to improve the quality of students’ learning and understanding is not new. For more than twenty years, concept mapping (Sowa, 1994; Jonassen, 1998; Conlon, 1997) has been used to provide structures and mental models that support the process of teaching and learning. As such, the visual representation of general domain concepts facilitates and supports student understanding of both substantive and syntactic knowledge. Many teachers, especially those who teach science and engineering courses, operate as a knowledge analysts or

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knowledge engineers, by making the skeleton of the discipline being studied visible and showing the domain’s conceptual structure (Kinchin, 2006). Often, this structure is called “ontology”. However, an ontology-based approach to knowledge representation in pedagogy is a relatively new development. Ontology is a set of distinctions we make in understanding and viewing the world. There are numerous definitions of this seminal term (Neche et al ,1991; Gruber, 1993; Guarino et al, 1995; Gomez-Peres, 1999). Together, these definitions clarify the ontological approach to knowledge structuring while leaving enough room for open-ended, creative thinking. So, for example, ontological engineering can provide a clear representation of a course structure, main concepts, approaches, terms and their inter-relationships. Many researchers and practitioners argue about the distinctions between an ontology and a conceptual model. We hold that an ontology corresponds to the analyst’s view of the conceptual model, but is not de facto the model itself. There are more than one hundred techniques and notations that help to define and visualise conceptual models. Ontologies are now held to be the most universal and easily shared forms of such modelling. It originated in the field of knowledge engineering (Boose, 1990; Wielinga, Schreiber, Breuker, 1992), then it was transferred to knowledge management (Fensel, 2001). Ontologies are useful structuring tools, in that they provide an organising axis along which every student can mentally mark his vision in the information hyperspace of domain knowledge. Frequently, it is impossible to express the information as a single ontology. Accordingly, subject knowledge storage provides for a set of related ontologies. Some problems may occur when moving from one ontological space to another, but constructing meta-ontologies may help to resolve these problems. A meta-ontology provides a more general, description dealing with higher level abstractions. Figure 1 illustrates different ontology classifications in the form of a mind map. Mind-mapping (Buzan, 2005) and concept mapping (Novak & Canas, 2006) are now widely used for visualising the ontologies at the design stage. A mind map is a diagram used to represent words, ideas, tasks, or other items linked to and arranged around a central key word or idea. The central topic is located in the middle, with related topics branching out from it. Ideas are further broken down and extended until you’ve fully explored each branch of your map. Mind maps are used to generate, visualise, structure, and classify ideas, and as an aid in study, organisation, and writing. The elements of a given mind map are arranged intuitively according to the importance of the concepts, and are classified into groupings, branches, or areas, with the goal of representing semantic or other connections between pieces of information. Some of the earliest examples of mind maps were developed by Porphyry of Tyros, a noted thinker of the 3rd century, as he graphically

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visualised Aristotle’s concept categories. The philosopher Ramon Llull (1235 1315) also used mind maps. British popular psychology author Tony Buzan claims to have invented modern mind mapping. Buzan argues that, while 'traditional' outlines force readers to scan from left to right and top to bottom, readers actually tend to scan the entire page in a non-linear fashion. Buzan also uses popular assumptions about the cerebral hemispheres in order to promote the exclusive use of mind mapping over other forms of note-taking. A concept map is a diagram showing the relationships among concepts. Such maps are graphical tools for organising and representing knowledge. They include concepts, usually enclosed in circles or boxes of some kind, with relationships between concepts indicated by a connecting line linking two concepts. One way to use concept maps for instruction is to have students create maps before and after each instructional activity in a unit. This allows teachers to determine the level of knowledge prior to teaching and to determine whether the desired objectives were met after instruction was completed, while allowing students to review their knowledge. Teachers can also discuss and correct any conceptual errors that their students may have made. Concept maps represent and clearly name both objects and relations between objects, while mind maps only present objects and the hierarchy that exists among them. The mind map and concept map modelling language involved is based on a class-based, object-oriented language that supports the classification and parameterisation of knowledge entities. As such, it can be applied to developing those tutoring systems where general understanding is more important than factual details. We used this type of approach in our teaching of artificial intelligence and cognitive science. Ontology design also may be used as an assessment procedure, where it is used for expressive as opposed to exploratory learning. These are different and complementary modes of learning. Exploratory tools allow learners to investigate models of a given domain which are different from their own and so examine consequences and conflicts. Expressive tools give the students the opportunity to express their own models of reality and learn by representing, exploring and reflecting on their consequences. For both formative and summative assessment purposes, students, through creating ontologies and explaining the processes involved, can clearly indicate the extent and nature of their knowledge and understanding. Knowledge entities that represent static knowledge of the domain are stored in the hierarchical order in the knowledge repository and can be reused by others. At the same time, those knowledge entities can be also reused in description of the properties or methodological approach as applied in the context of another, related knowledge entity.

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By static knowledge, we mean that the main knowledge patterns don’t change at some particular period of time. They are supposed to be the same during a substantial time period.

Fig. 1. Summarising ontology classifications in a mind-map

Ontological Engineering For Courseware Design Ontological engineering, as presented in Figure 2, covers all the issues of ontology development and its applications. But it is a unfortunate tradition that the technological aspects are much more explored than the methodological ones. Ontology development still faces the knowledge acquisition bottleneck problem, as it was described in the work (Guarino, Giaretta, 1998). The teacher, as the ontology developer, encounters the additional problem of not having any sufficiently tested and generalised methodologies, which would recommend what activities to perform and at what stage of the ontology development process. That is, each ontologist usually follows his/her own set of principles, design criteria, and steps in the ontology development

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process. Even during the last decade, when some effective tutorials on ontology development were presented (Noy, 2001; Mizoguchi & Bourdeau, 2000), the absence of structured guidelines and methods hindered the development of shared and agreedupon ontologies within and between teams, the extension of a given ontology by others, and its reuse in other ontologies and final applications.

Fig. 2. Ontological Engineering

Until now, few domain-independent methodological approaches have been reported for building ontologies (Fensel, 2000; Noy, McGuinness, 2001; Dicheva, Aroyo, 2004). One thing that these methodologies have in common is that they start from the identification of the purpose of the ontology and the need for acquiring knowledge of the domain. However, having acquired a significant amount of knowledge, major researchers are proposing a formal language expressing the idea as a set of intermediate representations and then generating the ontology using translators.

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These representations bridge the gap between how people see a domain and the languages in which ontologies are formalised. The conceptual models are implicit in the implementation codes. A re-engineering process is usually required to make the conceptual models explicit. Ontological commitments and design criteria are implicit in the ontology code. This chapter proposes a clear, explicit approach to ontology design, using a visual, iconic representation in the form of a tree or set of tree diagrams/structures. Figures will illustrate the idea of how ontology can bridge the gap between the chaos of unstructured data and a clearly mapped representation. However, ontology developers who are unfamiliar with or simply inexperienced in the languages in which ontologies are coded, (e.g. DAML, OIL, RDF) may find it difficult to understand how such ontologies have been created, and, conversely, even how to build a new ontology. At the basic level of knowledge representation, within the context of everyday heuristics, it is easier for educationalists to simply draw the ontology using conventional “pen and pencil” techniques. It is useful and illuminating to allow learners to create multiple representations of the same content (perhaps using different tools). However, for more sophisticated and elaborate knowledge representation it is necessary to master appropriate programming, and the language involved.

A Simple Recipe for Ontology Design While in major works the emphasis is placed on ontology specification (or coding), we would like to once again elucidate the essentials of ontology capture in the simplest form, as a recipe “for dummies”: A. Goals, strategy, and boundary identification: The first step in ontology development should be to identify the purpose of the ontology and the knowledge of the domain that needs to be acquired. It is important to be clear about what type of ontology (see Figure 1) is being built (taxonomy, partonomy, genealog, etc.) and what level of granularity characterises the concepts. We also need to elucidate the scope or “boundaries” of the ontology, before compiling a glossary. That effort is made at this step, as it affects the following stages of the design. B. Glossary development or meta-concept identification: This time-consuming step is devoted to gathering all the information relevant to the domain in question. The main goal of this step is selecting and verbalising all of the essential objects and concepts in the domain. A battery of knowledge elicitation techniques may be used, from interviews to free association word lists.

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C. Laddering, including categorisation and specification: Having all the essential objects and concepts of the domain in hand, the next step is to define the main levels of abstraction. Consequently, the high level hierarchies among the concepts should be revealed and the hierarchy should be represented visually on the defined levels. This could be done via a top-down strategy, by trying to break the high level concept from the root of the previously built hierarchy, by detailing and specifying the instance of concepts. Revealing a structured hierarchy is one of the main goals at this stage. Another way is generalisation via a bottom-up structuring strategy. Associating similar concepts to create meta-concepts from leaves of the aforementioned hierarchy is one way of doing this. The main difficulty is forming categories by creating high level concepts and/or breaking them into a set of detailed ones where it is needed. Categorisation is one of the higher cognitive activities, and it is a teacher’s job to create and label all of the main categories, sub-categories, and concepts of the teaching content. The learners may be involved in this process as well. Collaborative work in groups sometimes produces a positive synergetic effect. Such approach doesn’t go against the mainstream constructivist style of university learning. It merely places the emphasis on structuring methodology. D. Orchestration: This term refers to the harmonious organisation (Orchestration, 2008). The final step is devoted to updating the visual ontology structure by excluding any excessiveness, synonymy, and contradictions. The main goal of this final step is to create a beautiful or harmonious ontology. Beauty is a characteristic of an object, or idea that provides a perceptual experience of pleasure, meaning, or satisfaction [Beauty, 2008]. Beauty is studied as part of aesthetics, sociology, social psychology, and culture. Of course there are cultural differences in the perception of beauty. The experience of “beauty” often involves the interpretation of some entity as being in balance and harmony with nature, which may lead to feelings of attraction and emotional well-being. Because this is a subjective experience, it is often said that “beauty is in the eye of the beholder” [Martin, 2007]. But in many cases, learners and teachers agree on what structures are . The ideas of “beautification” are well known in basic studies, beginning from the search for a beautiful formula, model, or result. Beauty was always a very important criterion for scientific truth. We believe that harmony and clarity are the main properties that make an ontology beautiful.

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Visual Ontology Orchestration Bearing in mind that teaching ontologies are to be used not only as a knowledge component of the courseware system, but also as a mental tool for comprehensiveness and better understanding, we tried to follow the principle of a “good shape” (or beauty) that is not new in basic scientific abstraction and modelling (e.g. physics, chemistry, etc.). It is difficult to give the formal definition of this concept, but it features the imprecise sense of harmonious or aesthetically pleasing proportionality and balance. The most substantial impulse to it was given by the German psychologist Max Wertheimer. We have partially transferred his criteria for a good Gestalt (image or pattern) (Wertheimer, 1945) to ontological engineering: • Law of Pragnanz (the law of good shape) – the organisation of any structure in the nature or cognition will be as good as the prevailing conditions allow. ‘Good’ here means regular, complete, balanced, and/or symmetrical. • Law of Parsimony – the simplest example is the best (the Ockham’s razor principle): entities should not be multiplied unnecessarily. In the case of building ontological hierarchies, we have to keep in mind that a well-balanced hierarchy corresponds to a strong and comprehensible representation of the domain knowledge. We enlist below some tips that we consider useful in formulating the idea of “harmony”(Gavrilova, 2003): • Concepts of one level should be linked to their parent concept by one type of relationship, for example, “is-a”, “has part”, etc. This means that concepts of one layer have a similar nature and level of granularity. • The ontology tree should be balanced, that is, the depth of the paths in the ontological tree should be more or less equal (±2 nodes). This will also insure that the general layout is symmetrical. Asymmetry means that shorter branch is less investigated or longer one is too detailed (see Figure 3). • Cross-links should be avoided as much as possible. Moreover, when building an ontology, which is used for visualisation and browsing information, it is important to pay attention to clarity. Minimising the number of concepts is the best tip, according to the Law of Parsimony. The maximal number of branches and number of levels may follow Miller’s “magical number” (7±2), which is related to the human capacity for processing information (Miller, 1956). “Beautification” bias works as a strong methodological approach that helps to find points (nodes) of “growth”, “weak” branches, inconsistency, and excessiveness. But, in practice, specific features of domain knowledge may be of higher priority than design principles.

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We have produced several simple hints to refine and illuminate the ontology’s design stage. • Use different font sizes for different levels. • Use different colours to distinguish particular subsets or branches. • Use a vertical layout of the tree structure/diagram. • If necessary, use different shapes for different types of node.

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We have already developed more than 20 teaching ontologies for the “AI” course and some other courses, e.g. some of have developed together with Peter Brusilovsky, Sergei Sosnovsky and Rosta Farsan for a course on C-programming (Brusilovsky, Gavrilova, Sosnovsky, 2005). They are based on WHAT-knowledge conceptual structures to help students understand and remember the main concepts of the course. Also several research ontologies were developed to help the research community to generalise their shared understanding; the domains were “user modelling” (with Peter Brusilovsky and Michael Yudelson) (Brusilovsky et al., 2005) and “ontologies in education (with Darina Dicheva and Sergey Sosnovsky) (Gavrilova et al., 2005). That work is still in progress, as the domains are changing very quickly. Since we are speaking about the pre-design stage of creating lightweight ontologies (without formalising them into OWL or other language), the use of any available graphical editors may be helpful. These editors work as powerful assistants. We got our best results when using mind mapping and concept mapping tools. There are many popular tools that have been developed to create Mind Maps. These tools are limited in the sense that they typically cannot produce any other type

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of graph structure and do not allow for explicit linking terms within or across branches. Mind Maps are often very colourful, so the best commercial software has sophisticated colour and imaging options. These tools are very handy, simple and clear, so their features make them more user-friendly and produce more impressive representations. Some of these tools have become fairly sophisticated in terms of linking to Internet resources, adding notes, and support for collaborative use. The most widely used and developed versions include: 1. Mind Manager from MindJet http://www.mindjet.com/index.shtml, 2. Visual Mind http://www.visual-mind.com/ 3. VisiMap by CoCo Systems http://www.coco.co.uk./. http://www.coco.co.uk./prodvm.html 4. Mind Mapper from SimTech USA http://www.mindmapper.com/ 5. Concept Draw has a Mind Mapping product, as well as general drawing and diagramming products that integrate with Visio. More information is available at http://www.conceptdraw.com/en/products/CDPMindMap/. However, any effective computer program for ontological engineering should perform the functions described for structuring the stages of a subject domain. Accordingly, it should correspond to the phenomenological nature of the knowledge elicitation involved, using different appropriate algorithms. This program must support the knowledge engineer through incorporating "rules of the game" that are clear, transparent, and functional. Ideally, the knowledge engineer should be able to tailor the program to his or her specific requirements. As such, each analytical stage may be represented visually. accurately modelling the knowledge domain, an element that has already been realised in some commercial expert system shells. To achieve these goals, a set of special visual tools were developed, and named CAKE-2, VICONT, PORTO, VITA (with Tim Geleverya, Alex Voinov, Vitaliy Fertman and Vladimir Gorovoy). They illustrate the idea of knowledge mappability as applied to data extraction, analysis, and structuring for the design of heterogeneous knowledge bases (Gavrilova et al, 2000–2009).

Developing Teaching Ontologies We have already developed more than 20 teaching ontologies for the courses “Artificial Intelligence” and “Knowledge Engineering” (Gavrilova, Stash, Texier, 2001; Gavrilova, Dicheva, Sosnovsky, Brusilovsky, 2005). They are based on WHATknowledge conceptual structures to help students understand and remember the main concepts of the course.

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We can offer different types of teaching ontologies which substantially can aid effective learning: • Main concepts ontology (or conceptual structure), • Historical ontology (genealogy), • Partonomy of the discipline, where the main relation between learning objects is “has_part’ • Taxonomy of theories, methods and techniques, etc. Figure 4 illustrates the ontology that describes the skeleton of the “knowledge elicitation” problem for expert systems development. It integrates the main theoretical and practical issues of this multi-disciplinary and multifaceted area. This ontology is rather subjective, as it summarises the author’s 20-year experience in working as a knowledge analyst and tutor. This ontology combines two main types of relations: A_kind_of and A_part_of, representing the so-called mixed ontology (mix of partonomy and taxonomy). It took more than 5 years of research and teaching to elaborate this structure.

Fig. 4. Ontology of “Knowledge Elicitation” domain

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The ontology-based approach is universal. We also used it for the methodological scaffolding of some other courses, e.g. some were used together with Peter Brusilovsky, Sergei Sosnovsky and Rosta Farsan for a course on C-programming (Brusilovsky, Gavrilova, Sosnovsky, 2005; Gavrilova, Farzan, Brusilovsky, 2005) at the University of Pittsburgh. In this section, we describe our attempt to develop ontology for C programming language following the aforementioned 4-steps algorithm. We have tried to report the exact practical procedures we followed at each step, by including all the visual structures.

Steps A and B: Goals Identification & Glossary Development As previously mentioned, the first steps in building an ontology consist of strategic planning and collecting information in the domain. Then glossary building of terms for the domain is conducted. To build a glossary for teaching an introductory C programming course, we collected the terms from two different types of resources: closed-corpus material and open-corpus material. The closed corpus materials are in the form of the lecture notes of Professor Brusilovsky that are specially designed for the course. The open corpus materials include several online tutorials in C programming. We extracted the terms from the lecture notes manually by carefully reviewing the lecture handout. The terms from opencorpus material were extracted automatically (Brusilovsky & Rizzo, 2002). Consequently, we tried to combine the automatically extracted terms with manually extracted terms to build a single glossary. Figure 5 presents the combined unsorted glossary.

Step C: Laddering: Building an Initial Mind Map Structure At this step, an initial visual structure of the glossary terms was built and examined. The main goal of this step is to create the sets of preliminary ideas and notions and to categorise those terms into concepts. A mind map can be a useful visual structure for this step. Figure 6 presents the mind map of that initial categorisation. Since the categorisation in this step was preliminary, some terms might not fit into any of the initial categorisations. We should mention that the categorisation in this step was done entirely manually, by Rosta Farzan. However, we employed lecture notes, which were used to build the glossary in the previous step, and to build the initial categories as well. We can consider the lecture notes, as expert help, to design the ontology. This is due to the fact that the lecture notes were designed by the expert who taught the course for several years.

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Fig. 5. Glossary of the terms for teaching C programming

Fig. 6. Trivial categorisation

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Figure 7 represents the details of our initial categorisation of the terms into the concept shown in Figure 8. The visual structures presented at this step illustrate the idea of how an ontology can facilitate the transition away from the chaos of unstructured data presented in the glossary and be a clear means of showing mapped representations.

Fig. 7. Details of first level categorisation

Another good example of this bridge can be our ontology of Italian artistic schools, which is built from a chaos of names of great Italian artists (Gavrilova, Kurochkin, Veremiev, 2004). It was made using several guides, books and encyclopae-

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dias on Italian art. The ontology presents the genealogy of three schools of painting (Paduan, Florentine and Venetian) in visual form. At the same step, we composed more precise concepts and hierarchies by analyzing the glossary and previously built visual structure. First we employed a top-down design strategy to create meta-concepts such as “Date,” “Structure,” and “IO”. Then, using the bottom-up strategy, we tried to fit the terms and concepts into a meta-concept. Moreover, we created relationships between the concepts. A concept map is the most useful visual structure for the representation of the results of this stage, since it makes it possible to define the relationship, in addition to building the hierarchy. The output of this step is a large and detailed map, which covers the course in a hierarchical way. Because of the huge size of this concept map it is difficult to include it in the chapter. However, since this ontology is designed for teaching purposes it is important to offer both the overall picture and a general hierarchy as well. Therefore, based on the detailed concept map, we built the general ontology shown in Figure 8.

Fig. 8. General ontology

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Step D: Orchestration As described in the algorithm, the final step is devoted to making the ontology beautiful. We used the aforementioned practical tips and hints to harmonise the design of an ontology. In addition, we re-built the general ontology, while taking the harmony and clarity factors into consideration. Comparing Figures 8 and 9, one can see these changes (e.g. the concept “I/O”- (input/output) was enriched in order to correspond to the level of detailing of the “Data” and “Structure” concept branches). Consequently, we tried to balance the depth of the branches by adding one more level to the “IO” branch. Another feature of harmony is having the same relationship at each level. Since this is not easy to achieve, we tried to differentiate the level of the nodes based on the relationships in the same depth. For example, all nodes with the “has” relationship are at the same level and all the nodes with the “has part” relationship are also at the same level. Moreover, to achieve clarity, we removed all unnecessary nodes and used the standard relationships that are easy to understand. After using the aforementioned recipe, the core ontology of the lecture course by Peter Brusilovsky was developed (see Figure 9).

Fig. 9. Harmony and clarity in the ontology

It is interesting that similar work done by another post-graduate student from the same research group, Sergey Sosnovsky (see Figure 10) leads to a totally different

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ontology (Sosnovsky, Gavrilova, 2006). This fact proves the subjective nature of ontology design once again, especially on the upper levels of domain ontologies. While the leaves of the tree may be very similar, the branches may vary both in number and name. It may be explained by the different style and manner of the categorisation procedure used by the analysts. Categorisation deals with creating classes of objects that share common features. If two analysts see different features they will compose different categorisation trees. They both may be correct. However, there is no visible way to assess the quality of those ontologies, besides finding a super-expert who may become the judge.

Fig. 10. Top Levels of the Educational Ontology of C Programming

Several research ontologies were also developed to help the research community to generalise their shared concepts, the domains were “user modelling” (with Peter Brusilovsky and Michael Yudelson, 2004) and “ontologies in education” (with Darina Dicheva, 2005). That work is still in progress, as the domains are rapidly changing.

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Future Research Directions All the visual models of knowledge are node-link representations, in which ideas are located in nodes and connected to other, related ideas through a series of labelled links. Research on knowledge mapping in the last decade has produced a number of consistent and interesting findings (O’Donnell et al., 2002; Dicheva, Dichev, 2007). Students recall more central ideas when they learn from a mind map or ontology than when they learn from text, and those with low verbal ability or low prior knowledge often benefit the most. The use of ontology engineering also appears to amplify the benefits associated with a structured approach. Learning from maps is enhanced by active processing strategies, such as summarisation or annotation, and by designing maps according to gestalt principles of organisation. Fruitful areas for future research on ontology mapping include examining whether visual representations reduce cognitive load, how map learning is influenced by the structure of the information to be learned, and the possibilities for transfer. Future research will be devoted to the theoretical and methodological issues of categorising and laddering processes. It seems to be challenging to study the feasibility of using the suggested approach in new forms of teaching, e.g. blended education. Visual mapping may also be used in different teaching assignments, such as writing research articles or reviewing books and papers. Great interest in the visual approach is being seen in many disciplines, and the new graphical models are coming into vogue. These models are roadmaps or strategic plans (Alitek Consulting, 2003), knowledge maps (O’Donnell et al., 2002) that show where knowledge is stored in companies, topic maps aimed at the representation and exchange of knowledge, with an emphasis on making information easy to find (Dicheva, Dichev, 2005) and many others. Our next projects are devoted to the study of the deeper cognitive basics of structuring processes, and are aimed at developing technologies and tools that scaffold the visual design and representation of learning ontologies.

Conclusion The approach described here and the numerous studies by many educationalists (Canas et al, 2003) allow us to sketch some patterns of how to enhance the educational effectiveness of visual models. First, when knowledge mapping is used in a course of instruction, it is better for it to be an integral, on-going feature of the learning process, not just some isolated “add-on” at the beginning or end. In this regard, ontology mapping appears to be particularly beneficial when it is used in an on-going way to consolidate or crystallise educational experiences in the classroom, for example, a lecture,

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demonstration, or laboratory experiment. In this mode, learners experience an educational event and then use visual mapping in a reflective way, to enhance knowledge acquisition from the event. There is also some indication that learning is enhanced when, in the course of knowledge engineering, learners adopt an active, deep, and questioning process. We can now observe how many new visual models (roadmaps, knowledge maps, topic maps, etc.) have become popular in the learning process. Some of them also have an ontological base. It seems that visual ontological design will be a must in courseware development in the future. Second, our research stresses the role of ontology orchestration for developing ontologies quickly, efficiently and effectively. We follow David Jonassen’s idea of “using concept maps as a mental tool”. The use of a visual paradigm to represent and support the teaching process not only helps a professional tutor to concentrate on the problem rather than on the details, but also enables learners and students to process and understand great volumes of information. The development of beautiful knowledge structures in the form of ontologies provides learning support and scaffolding that may improve student understanding of substantive and syntactic knowledge. As such, they can play a part in the overall pattern of learning by facilitating, for example analysis, comparison, generalisation, and transferability of understanding to analogous problems. Therefore, the visual knowledge structure editors provide a two dimensional, iconic model that represents the teacher’s understanding of key elements in the subject. Third, as an assessment tool, ontologies can be used to visualise and analyze the student’s knowledge and understanding. Through visual inspection of the ontology, it is possible to detect gaps and misunderstandings in the student’s cognitive model of the learnt knowledge. However, there is not much consensus yet about the design and orchestration of the courseware structure that should be used. Furthermore, in many cases, it is not known what the structure of socially legitimate knowledge pattern looks like, and how a particular instance of a knowledge model is deviating from that “ideal” state (e.g. the teacher’s view). But teachers are individuals, and they may disagree among themselves.

Acknowledgements Thanks to Piter Brusilovsky, Rosta Farzan and Sergey Sosnovsky for fruitful discussions and collaboration on the C-language ontology. Special thanks to Joshua Grove for his kind assistance in checking and editing. This work was partially supported by grants from the Russian Foundation for Basic Research and SPbSU.

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Schreiber, G. (2000). Knowledge Engineering and Management: The CommonKADS Methodology. MIT Press. Sosnovsky S., Gavrilova T. (2006). One Approach to Development of Educational .Ontology for C-Programming // Int. Journal “Information Theories and Applications”, vol.13, N4, 303-308. Sowa, J. F. (1984). Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley, Reading, Massachusetts. Tu, S., Eriksson, H., Gennari, J., Shahar, Y. & Musen M. (1995). OntologyBased Configuration of Problem-Solving Methods and Generation of KnowledgeAcquisition Tools. Artificial Intelligence in Medicine, N7, 257-289. Vestal, W. (2005). Knowledge Mapping: The Essentials for Success. APQC, 2005. Wielinga, B., Schreiber, G. & Breuker J. (1992). A Modelling Approach to Knowledge Engineering. In Knowledge Acquisition, 4 (1), Special Issue, 23-39. Werthheimer, M. (1945). Productive Thinking, New York: Harper Collins.

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Appendix C. Business Enterprise Ontologies Gavrilova, T., Laird, D. Practical Design of Business Enterprise Ontologies // Industrial Applications of Semantic Web (Eds. Bramer M. and Terzyan V.), Springer, 2005. – pp. 61–81.

Abstract This paper presents one approach to developing enterprise ontologies. The underlying research framework is the pursuit of a methodology that will aid the process of knowledge structuring and practical ontology design, with an emphasis on visual techniques. In order to illustrate the proposed technique, the development of a practical ontology of information technology skills for a human resources knowledge management system is described. Key words: Ontology, Visual Knowledge Engineering, Knowledge Acquisition, Knowledge Management.

Introduction Top managers and IT analysts are continually challenged by the need to analyze massive volumes and varieties of multilingual and multimedia data. This situation is not limited to e-business, but is seen in nearly all companies and institutions. Challenges have fuelled opportunities for analytic tool developers, educators, and business process owners that support analytic communities in the management of knowledge, information and data sources. Company staff and employees require support and guidelines for sharing knowledge about information analysis, theories, methodologies and tools. Knowledge management (KM) is one of the powerful approaches to solving these problems. The idea of using visual structuring of information to improve the quality of user learning and understanding is not new. Concept mapping has been used for more than twenty years1,2,3 in system design and development, in order to provide structures and mental models to support the knowledge-sharing process. As such, the visual representation of general corporate business concepts facilitates and supports company personnel’s understanding of both substantive and syntactic knowledge. An analyst serves as a knowledge engineer by making the skeleton of the company’s data and knowledge visible, and showing the domain’s conceptual structure. At the present time, this structure is called an ontology. However, ontologybased approaches to business are relatively new and fertile research areas. They origi-

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nated in the area of Knowledge Engineering4,5, then evolved into the separate discipline Ontology Engineering6,7. The discipline of Knowledge Engineering traditionally emphasised and rapidly developed a range of techniques and tools including knowledge acquisition, conceptual structuring, and representation models8,9. These developments have underpinned an emerging methodology that can bridge the gap between the ability of the human brain to structure and store knowledge, and the knowledge engineers’ ability to model that process. For practitioners, however, knowledge engineering is still a rather new, eclectic domain that draws upon a wide range of areas, including cognitive science, etc. Accordingly, knowledge engineering has been, and still is, in danger of fragmentation, incoherence, and superficiality. Since 2000, researchers have demonstrated major interest in building customised tools that aid in the process of knowledge capture and structuring. This new generation of tools, such as Protégé, OntoEdit, and OilEd, is concerned with visual knowledge mapping that facilitates knowledge sharing and reuse10,11,12. The problem of iconic representation has been partially solved by developing knowledge repositories and ontology servers, where reusable static domain knowledge is stored. Ontolingua, Ontobroker, and many others, are examples of such projects13,14. The use of ontologies has special value for companies where specialists reuse domain ontologies in order to support the business protocols that are grounded in the domain’s problem-solving methodology. Therefore, the basic idea is to allow experts to model both domain and problem-solving knowledge using the same visual language. Knowledge entities that represent static knowledge of the domain are stored in hierarchical order in the knowledge repository, and can be reused by others. At the same time, those knowledge entities can also be reused in description of the properties or a methodological approach, as applied in the context of another related knowledge entity. The concept map modelling language involved is based on a class-based, object-oriented language that supports the classification and parameterisation of knowledge entities. This paper proposes a practical approach to the design of business ontologies. The underlying research framework is the pursuit of the use of visual iconic representation and diagrammatic structures, with an emphasis on visual design. To facilitate clearer understanding of the methodology, the process of developing a practical ontology of information technology knowledge and skills is described. In the remainder of the paper, we will describe some theoretical issues regarding ontological engineering and present our proposed methodology for ontology design. Moreover, we will describe a detailed practical example using the proposed methodology. In conclusion, we will provide insight through discussion of current and possible future work.

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Using Ontological Engineering for Business Applications We must begin the discussion of theoretical issues of ontological engineering by developing a definition of ontology from literature within the field.

Ontology Definition Ontology is a set of distinctions we make in understanding and viewing the world. There are numerous, well-known definitions of this seminal term15,16,17, that may be generalised by such definition: “Ontology is a hierarchically structured vocabulary describing a domain that can be used as a skeletal foundation for a knowledge base”. This definition clarifies the ontological approach to knowledge structuring, while leaving sufficient room for open-ended, creative thinking. For example, ontological engineering can provide a clear representation of a company’s structure, human resources, physical assets, and products, and their interrelationships. Many researchers and practitioners argue about the distinctions between ontology and the user’s conceptual model. We believe that ontology corresponds to the analyst’s view of the conceptual model, but is not the de facto model. Ontology, as a useful structuring tool may greatly enrich the business modelling process, providing users of KM-systems with an organising axis to help them mentally mark their vision of domain knowledge.

Creating Ontologies for Business Use Ontology creation faces the knowledge acquisition bottleneck problem. The ontology developer frequently encounters the additional problem of not utilising sufficiently tested and generalised methodologies, which would recommend what activities to perform and at what stage of the ontology development process. An example of this can be seen when each development team generally follows its own set of principles and design criteria, and enters the ontology development process. The lack of structured guidelines and methods hinders the development of shared and agreed-upon ontologies within and between the teams. Moreover, it makes the extension of a given ontology by others and its reuse in other ontologies and final applications difficult18. Several effective, domain-independent methodological approaches have been reported for building ontologies6,7,19. What these approaches have in common is that they consistently begin with the identification of the purpose of the ontology, and the need for acquisition of domain knowledge. However, having acquired a significant amount of knowledge, major researchers propose a formal language expressing the

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idea as a set of intermediate representations and then generating the ontology using translators. These representations bridge the gap between how people see a domain and the languages in which ontologies are formalised. The conceptual models are implicit in the implementation codes. A re-engineering process is usually required to make the conceptual models explicit. Ontological commitments and design criteria are implicit in the ontology code. Figure 1 presents our vision of the mainstream, state-of-the-art categorisation in ontological engineering20,21,22 and may help the knowledge analyst to figure out what type of ontology he/she really needs. We use Mindmanager™, as it proved to be a powerful visual tool.

Fig. 1. Ontology classification

Frequently, it is impossible to express company business information in a single ontology. Accordingly, company knowledge storage consists of a set of related ontologies. However, some problems may occur when moving from one ontological space to another that could be solved by constructing meta-ontologies that may help to resolve these problems.

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We can propose different types of ontologies that can support business applications: • Company organisational structure • Main concepts ontology (products, services, customers, skills, etc.), • Historical ontology (genealogy of owners, customers, products, services, etc.), • Partonomy of company knowledge • Taxonomy (methods, techniques, technologies, business-processes, skills, etc.) The concrete set of ontologies depends on personal vision, business application and awareness level of the system’s analysts and users. Generalising our experience in developing different business and teaching ontologies in the field of consulting, business modelling and information technologies23,24,25,26, we propose a four-step algorithm that may be helpful for visual ontology design. We are attempting to develop the ideas of Uschold and King’s skeletal methodology,22 emphasising the details of ontology capture, where visual representation works as a powerful mental tool2 in the structuring process. Visual form influences both analyzing and synthesising procedures in ontology development process. That is why we believe that the “beauty” of the ontology plays an important role in understanding of the knowledge.

Ontology Creation While in major research work, the emphasis has been placed on ontology specification, we would like to elucidate the essentials of ontology capture22, not coding.

Four-Step Algorithm Step 1. Goals, strategy and boundary identification: The first step in ontology development should be to identify the purpose of the ontology and the domain knowledge that must be acquired. It is important to be clear about why the ontology is being built and what its intended uses are22. We also need to define the scope or “boundaries” of the ontology, before compiling a glossary. It is also important to elucidate the type of ontology according to the classification in Figure 1, such as taxonomy, partonomy, and genealogy. That effort is done at this stage, as it affects the next stages of the design. Step 2. Glossary development or meta-concept identification: This time consuming step is devoted to gathering all the information relevant to the domain being described. The main goal of this step is selecting and verbalising all of the essen-

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tial objects and concepts in the domain. A battery of knowledge elicitation techniques may be used, from interviews to free association word lists. Step 3. Laddering, including categorisation and specification: Having all the essential objects and concepts of the domain in hand, the next step is to define the main levels of abstraction. Consequently, the high level hierarchies among the concepts should be identified and the hierarchy should be represented visually on the newly-defined levels. This could be done via a top-down strategy, by trying to break the high level concept away from the root of the previously-built hierarchy, by detailing and specification of instance concepts. Revealing a structured hierarchy is one of the main goals at this stage. Another way is generalisation via bottom-up structuring strategy. Associating similar concepts to create meta-concepts from leaves of the aforementioned hierarchy is one way to do this. The main difficulty is forming categories by creating high level concepts and/or breaking them into a set of detailed ones where it is needed. Step 4. Refinement: The final step is devoted to updating the visual structure by excluding any excessiveness, synonymy, and contradictions. As mentioned before, the main goal of the final step is to create a beautiful ontology. The ideas of “beatification” are well known in basic studies, beginning from the search for a beautiful formula, model or result. Beauty was always a very important criterion for scientific truth. We believe that harmony and clarity are what make an ontology beautiful.

Harmony To achieve harmony, we attempt to follow the Gestalt (good form) principles established by M. Wertheimer27: • Law of Pragnanz: organisation of any structure in nature or cognition will be as good (regular, complete, balanced, or symmetrical) as the prevailing conditions allow (law of good shape). • Law of Proximity: objects or stimuli that are viewed as being close together will tend to be perceived as a unit. • Law of Similarity: things that appear to have the same attributes are usually perceived as being a whole. • Law of Inclusiveness (W. Kohler): there is a tendency to perceive only the larger figure, and not the smaller, when it is embedded in a larger. • Law of Parsimony: the simplest example is best known as the Ockham’s razor principle (14th century): “entities should not be multiplied unnecessarily”.

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Conceptual balance  A well-balanced ontological hierarchy equates to a strong and comprehensible representation of domain knowledge. The example of ill-balanced ontology design (see Figure 2) shows that long branches are over-detailed, while shorter ones are under-investigated. In terms of the problem we are investigating, this can lead to a situation where some IT-skills will be described too precisely while other will be just briefly mentioned. An ill-balanced ontology often demonstrates a low level of professionalism on the part of the expert and/or knowledge analyst. However, it is a challenge to formulate the idea of a well-balanced tree. Here we offer some tips to help formulate the notion of “harmony”: • Concepts at one level should be linked with the parent concept by only one type of relationship, such as “is-a”, or “has part”. • The depth of the branches should be more or less equal (±2 nodes). • The general layout should be symmetrical. • Cross-links should be avoided as much as possible. Figure 2 illustrates the idea of balance.

Fig. 2. Well-balanced (A) and ill-balanced (B) ontologies

Clarity In addition to the principle of harmony, it is important to pay attention to clarity when building a comprehensible ontology. Clarity may be ensured through a number of concepts, and types of the relationships among the concepts.

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• • •

Minimising the number of concepts. The maximal number of branches and the number of levels should follow Miller’s magical number (7±2)28. Furthermore, the type of relationship should be clear and obvious if the name of the relationship is missing. Some tips to achieve visual clarity are described later, in section 4.4.

Developing a Practical Ontology In this section we describe the development of an ontology of information technology skills and knowledge, following the aforementioned 4-step algorithm. We have tried to report the exact practical procedures we followed at each step by including all the visual structures.

Step 1 - Purpose and use of the Ontology: It is important to identify the purpose and intended use of the ontology early in the process of its development22. The example ontology described throughout the remainder of this paper was developed to support a business application to address the following needs. Situation/Problem: A company is seeking to identify the knowledge and skills of each of its employees that are relevant to the work of the company. This data will allow the company to: • Identify the essential skills of the organisation • Develop a knowledge retention strategy to ensure that sufficient depth is present in the organisation in the event of resignation, retirement, or other loss of key employees • More effectively identify and utilise employee skillsets: • Allow employees to quickly find experts to address unique questions or problems. • Identify individuals in the company with the needed skill to work on new or expanding projects • Develop individual and organisation-wide training plans and strategies, based on the collective training needs of the whole enterprise. Solution: Use a network-based intranet application that allows employees to identify their individual skills and training needs. This application will make use of an ontology of skills that span the IT industry, and allow employees to select relevant skills and knowledge from that ontological presentation of skills, which they currently

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possess, or have a business-related need to acquire. Using the ontology in this way serves the following purposes: • Ensuring that each employee considers the entire range of IT Skills that he might possess, or that are relevant to the organisation. • Ensuring that data is entered uniformly into the system by each employee, with a consistent understanding of the meaning of each skill. This consistency allows subsequent searches of the employee skills database to find all cases of a selected skill, and the organisation’s training to be planned for specific or broad categories of skills. • Provides for a framework to visualise and better understand the relationships of skills that are relevant and critical to the success of the organisation.

Step 2 - Glossary Development As previously mentioned, the first step in building an ontology is collecting information in the domain in question and building a glossary of terms relevant to the domain. To build a glossary of information technology skills, we collected terms from two different types of resources: closed-corpus material and open-corpus material. The closed corpus materials are in the form the company’s job descriptions in the field of information technology, recent correspondence and status reports from the Information Systems department, and an inventory of skills generated by the organisation. The open corpus materials include the tables of contents and indexes of general information systems text references, categorisations and descriptions of computer and information science course offerings, and existing ontologies about the field of information technology. The terms and concepts from each of these sources were combined to build a single glossary (Table 1).

Step 3 - Laddering: Building an Initial Mind Map Structure In the third step, we built an initial visual structure of the terms in the glossary. The main goal of this step is the creation of a set of preliminary, high level concepts and the categorisation of the glossary terms in terms of those concepts. A mind map can be a useful visual structure for this step. Figure 3 presents the mind map of our initial categorisation. Since the categorisation in this step is preliminary, some of terms might not fit into any of the initial categorisation. We should mention that the categorisation at this step is done entirely manually. However, we employed the job descriptions, text glossary and table of contents, and groupings of university course offerings, which were used to build the glossary in the previous step, to build the initial categories as well. We can consider the groupings from these sources to be expert

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help in designing the ontology, because such groupings were established to make the presented information in these sources clear and easily accessible; traits that we desire in the finished IT Skills ontology. Figure 4 presents the details of our initial categorisation of terms, leading to the concept represented by Figure 3. The visual structures presented in this step illustrate the idea of how an ontology can facilitate the transition away from the chaos of unstructured data presented in the glossary, and be a clear means of showing mapped representations. Table 1. Sampling of a Glossary of IT Skills and Knowledge Personal Computers Maintenance

Peripherals Maintenance

Help Desk Support

Database Administration

Programming

Application Development

Cyber Security

Encryption

Commercial Software

Super Computing

Telecommunications

Training

Project Management

Graphics

Mobile Computing

Computer Architecture

Software Development Lifecycle

Quality Assurance

Human Factors in Systems

Human/Computer Interactions

Artificial Intelligence

Geographic Information Systems

Decision Support Systems

Data Mining

Information Storage and Retrieval

Programming Languages

Software Engineering

Algorithm Design

Computer Engineering

Visual Languages

Operating Systems

Document Processing

Information Processing Standards

Knowledge Representation

Legal and Ethical Issues

Expert Systems

Ontologies

Knowledge Management

Routers

Bridges

Network Switches

Computer Server Support

Virus Detection

Email Systems

Enterprise System Customisation

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Fig. 3. Trivial Categorisation

Later, we composed more precise concepts and hierarchies by analyzing the glossary and previously-built visual structure. First, we employed a top-down design strategy to create meta-concepts, such as Programming, Network Support, Project Management, etc. Then, using a bottom-up strategy, we tried to fit the terms and concepts into the meta-concept. Moreover, we created relationships between the concepts. A concept map is the most useful visual structure for the representation of the results of this stage, since it makes it possible to define the relationships in addition to building the hierarchy. The output of this step is a large and detailed map, which covers the domain hierarchically.

Fig. 4. Details of first level categorisation

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Next, based on the detailed concept map, we built the general ontology that is shown in Figure 5, utilising liberal relationship terms to link the concepts with detailed terms from the glossary.

Fig. 3. General ontology

Step 4: Refinement As described in the algorithm, the final step is devoted to making the ontology beautiful. The following are some practical tips that may be taken into consideration while refining an ontology, and are illustrated in Figure 6: 1. Use different font sizes for different strata 2. Use different colours to distinguish particular subsets or branches (this is not very clear on a black and white printout). 3. Use a vertical layout for the tree structure/diagram. 4. If necessary, use different shapes for different types of nodes. Moreover, we rebuilt the general ontology, while taking the harmony and clarity factors into consideration. Comparing Figure 5 and Figure 6 presents these changes. Another feature of harmony is having the same relationship in every level. Moreover, to achieve clarity, we removed all unnecessary nodes and used standard, consistent relationships.

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Fig. 4. Harmony and clarity in the ontology

Discussion Our research stresses the role of knowledge structuring for developing ontologies quickly, efficiently, and effectively. At a basic level of knowledge representation, within the context of everyday heuristics, it is easier for practitioners to simply draw the ontology using conventional “pen and pencil” techniques. However, for more sophisticated knowledge representations, our proposed 4-step ontology development process is proposed. The development and use of an ontology of IT Skills and Knowledge was illustrated in this paper to provide a concrete example of the proposed methodology. A more detailed version of the illustrated ontology will be integrated into the use of a Knowledge Management application, used to develop a map of critical skills and knowledge within a business enterprise. This awareness of the critical skills needed and possessed by the organisation will allow strategies to be developed to ensure the retention and most effective use of those critical skills. Without a comprehensive ontology to frame this investigation, valid and useful results would be far more difficult to achieve.

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In subsequent research, we plan to explore ways that ontology development and use can further improve visualisation of business needs, and deliver additional value for organisations. Such investigations will address the reduction of overlap between business units in an organisation, aligning recruiting efforts with actual business needs, development of job descriptions that accurately reflect the skills and knowledge truly needed for the success of the organisation, and a clearer understanding of the most critical and valued skills within the organisation.

Acknowledgments This work is partly supported by a grant No. 04-01-00466 from the Russian Foundation for Basic Research.

References 1. J.F. Sowa (1984) Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley, Reading, Massachusetts. 2. D.H. Jonassen (1998) Designing constructivist learning environments. In Instructional design models and strategies (C.M. Reigeluth (Ed), 2nd ed., Lawrence Eribaum, Mahwah, NJ. 3. T. Conlon (1997) Towards Diversity: Advancing Knowledge-based Modelling with Knowledge Acquisition. In Proceedings of 8th International PEG Conference, Sozopol, Bulgaria., pp.379-386. 4. J.H. Boose (1990) Knowledge Acquisition Tools, Methods and Mediating Representations. In Knowledge Acquisition for Knowledge-Based Systems (Motoda, H. et al., Eds), IOS Press, Ohinsha Ltd., Tokyo, pp.123-168. 5. P. Brusilovsky and R. Rizzo (2002), Map-Based Horizontal Navigation in Educational Hypertext. In Proceedings of Hypertext, University Of Maryland, College Park, USA 6. D. Fensel (2001) Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce. Springer. 7. R. Mizogushi and J. Bourdeau (2000), Using Ontological Engineering to Overcome Common AI-ED Problems. International Journal of Artificial Intelligence in Education, volume 11, pp.1--12. 8. H. Adeli (1994) Knowledge Engineering. McGraw-Hill, New York. 9. A. Scott, J.E. Clayton & E.L. Gibson (1994) A Practical Guide to Knowledge Acquisition, Addison-Wesley.

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10. Protégé, Stanford Medical Informatics. Accessed from http://protege.stanford.edu/ at December 07, 2004 11. OntoEdit, AIFB, University of Karlsruhe. Accessed http://www.ontoknowledge.org/tools/ontoedit.shtml at December 07, 2004

from

12. OilEd, S. Bechhofer and G. Ng Accessed from http://oiled.man.ac.uk/ at December 7, 2004 13. OntoBroker, Accessed from http://ontobroker.aifb.uni-karlsruhe.de/index_ob.html at December 7, 2004 14. Ontolingua, Stanford University. Accessed from http://www.ksl.stanford.edu/software/ontolingua/ at December 7, 2004 15. Neches, et al , (1991) Enabling Technology for Knowledge Sharing. AI Magazine, Winter, pp.36- 56. 16. T. Gruber (1993) A translation approach to portable ontology specifications. Knowledge Acquisition , Vol. 5, pp.199- 220. 17. A. Gómez-Pérez, M. Fernández-López, O. Corcho (2004) Ontological Engineering with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web. Springer. 18. N. Guarino & P. Giaretta (1998) Ontologies and Knowledge Bases: Towards a Terminological Clarification. In Towards Very Large Knowledge Bases: Knowledge Building & Knowledge Sharing, IOS Press, pp.25- 32. 19. B. Swartout, R. Patil, K. Knight & T. Rus (1997) Toward Distributed Use of Large-Scale Ontologies. In Ontological Engineering, AAAI- 97 Spring Symposium Series, pp.138- 148. 20. N. Guarino, C. Welty (2000) A Formal Ontology of Properties. In R. Dieng and O. Corby (eds.), Knowledge Engineering and Knowledge Management: Methods, Models and Tools. 12th International Conference, EKAW2000. Springer Verlag: 97-112. 21. R. Jasper and M. Uschold (1999). A Framework for Understanding and Classifying Ontology Applications. In Twelfth Workshop on Knowledge Acquisition Modelling and Management KAW'99. 22. M. Uschold, M. Gruninger (1996). Ontologies: Principles Methods and Applications. In Knowledge Engineering Review, Volume 11, Number 2. 23. T.A. Gavrilova & A. Voinov (1996) Visualised Conceptual Structuring for Heterogeneous Knowledge Acquisition. In Proceedings of International Conference on Educational Multimedia and Hypermedia, EDMEDIA'96, MIT, Boston, USA, pp.258-264.

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24. T. Gavrilova, A. Voinov (1998) Work in Progress: Visual Specification of Knowledge Bases, Lecture Notes in Artificial Intelligence 1416 “Tasks and Methods in Applied Artificial Intelligence”, A.P. del Pobil, J. Mira, M. Ali (Eds), Springer. - pp. 717-726. 25. T.A. Gavrilova, A. Voinov, E. Vasilyeva (1999) Visual Knowledge Engineering as a Cognitive Tool, Proc. of Int. Conf. on Artificial and Natural Networks IWANN’99, Spain, Benicassim. - pp.123-128. 26. T. Gavrilova (2003) Teaching via Using Ontological Engineering, Proceedings of XI Int. Conf. “Powerful ICT for Teaching and Learning” PEG-2003, St.Petersburg. – p. 23-26. 27. M. Wertheimer (1982) Productive Thinking. Univ. of Chicago Pr (Tx); Phoenix ed edition, 28. G. Miller (1956) The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information. The Psychological Review, vol. 63, pp. 81-97.

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Appendix D. Information mapping software 3D concept and mind maps 1. 3D Topicscape Student Edition 2. Conspicio Mindmapper 3. Morcego 3D Network Browser

Concept maps 4. 3D Topicscape Student Edition 5. Bubbl.us 6. Cayra 7. CmapTools 8. CoFFEE 9. Compendium 10. Conzilla 11. Glinkr 12. Hypergraph 13. Labyrinth 14. LifeMap 15. Visuwords 16. VUE (Visual Understanding Environment) 17. yEd

Diagrams, Flowcharts 18. Creately 19. Dia 20. DrawAnywhere 21. Gliffy 22. GraphViz 23. ImaginationCubed 24. Thinkature 25. Project Draw 26. Dabbleboard 27. yEd

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Hyperbolic trees 28. GraphViz 29. Hypergraph 30. Treeviz Information & knowledge management 31. 3D Topicscape Student Edition 32. eyePlorer 33. Graph Gear 34. JSViz 35. Protégé-Frames (protege) 36. Protégé-OWL (protege) 37. Xebece + 38. Visuwords Maps of Arguments, Belief, Idea support, Debates, Decisions and Influence 39. Argunet 40. Cohere 41. Debategraph 42. Prefuse Concept maps or mind maps? What is the right choice? 43. 3D Topicscape Student Edition 44. Bookvar 46. Cayra 48. CharTr 50. Creately 52. EDraw Mind Map 54. Ekpenso 55. FreeMind 56. Freeplane 57. Gliffy

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58. Glinkr 59. Hypergraph 60. Kdissert 61. Mind42 62. Mind Map Viewer (Eric Blue) 63. MindNode 64. MindRaider 65. Semantik 66. Text2Mindmap 67. Thinkature 68. ThinkGraph 69. Thoughtex 70. Tomboy mindmap 71. VYM (View Your Mind) 72. WikiMindMap 73. WoW (Web of Web) 74. Xebece + 75. Xmind 76. XWiki MindMap

Ontologies and Taxonomies 77. Jambalaya 78. Protégé-OWL (protege) 79. Dendroscope

Presentations 80. VUE (Visual Understanding Environment) Treemaps 81. Treeviz

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Whiteboard 82. ImaginationCubed 83. Thinkature Wiki-related 84. MindRaider 85. QwikiWiki 86. XWiki MindMap

With significant limitations in the Free version 87. InfoRapid KnowledgeMap 88. Lovely Charts 89. MindMeister 90. Mindomo 91. PersonalBrain 92. Prezi 93. Wisdomap

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Appendix E. “Knowledge engineering” course syllabus Synopsis This course introduces students to the practical application of intelligent technologies to different subject domains (business, social, economic, educational, humanities, etc.). It will give students insight and experience in the key issues of data and knowledge processing in various companies. In class and discussion sections, students will be able to discuss issues and tradeoffs in visual knowledge modelling, and invent and evaluate different alternative methods and solutions for better knowledge representation, understanding, sharing and transfer. It is targeted at managers of different levels, involved in any kind of knowledge work. The lecture course's goals are focused on using the results of multidisciplinary research in knowledge engineering, data structuring and cognitive science into information processing and modern management. The hands-on practice will be targeted at e-doodling with Mind Manager and Cmap software tools.

Organisation of the course Program

Master in International Business and MITIM

Course status

Core

Workload

6 ECTS, 45 hours of classes

Prerequisites

none

Teaching methods

Lectures, discussions, short tests, case studies. The lectures will be important but the emphasis will be on learning through training, games, discussions and short tests. A good deal of the course will focus on self-reflection and selfformalising of knowledge, training analytical and communicative abilities, discovery, creativity, achieving new perspectives, synthesising evidence from disparate sources, and gaining new insights into this fascinating new field. The case studies discussed in class will be selected on a timely basis to reflect contemporary issues and current topics. There will be a series of vivid exercises using cognitive training techniques. All classes are scaffolded by hand-outs and PowerPoint presentations.

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Course objectives Students will be introduced to major issues in the field and to the role of the knowledge analyst in the development of strategic information systems. Significant attention will be directed to relating knowledge engineering to other professional areas, e.g., information management and business administration. Students will gain an understanding of the role of knowledge engineering and knowledge management in companies and organisations and in professional communications and decision-making by members of an organisation. The main education gain for students will be the practical skill of visual business information structuring with the use of special software (mind mapping and concept mapping). The course features knowledge engineering as the methodology of data and knowledge processing. Knowledge engineering will be defined as an information elicitation and structuring methodology for different domains. The course will examine a number of related topics, such as: • •

• •

system analysis and its applications; the relationships among, and roles of, data, information, and knowledge in different applications, including marketing and management, and the varying approaches needed to ensure their effective implementation and deployment; characteristics of theoretical and methodological topics of knowledge acquisition, including principles, visual methods, issues, and programs; defining and identifying cognitive aspects of knowledge modelling, and visual representation (mind mapping and concept mapping techniques).

Course content •

•

•

Topic 1. A Brief Introduction to Systems Analysis (SA) and Information Management (IM). Systems, elements, relations, hierarchy. Information Management: the modern approach. History: a brief synopsis of its evolution. The main branches of SA and IM. Working with information Topic 2. Intelligent technologies in IM. A short history. Knowledge-based systems. Expert systems. Machine learning. Data mining and Knowledge discovery Topic 3. Introduction to Knowledge Engineering (KE) and Visual Approach. Knowledge and data. Practical knowledge structuring: the visual approach. Mental models. Mind maps and mind-mapping tools. Concept maps and tools. Roadmaps and knowledge maps.

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•

•

•

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Topic 4. Knowledge representation and practical knowledge engineering. Classification of knowledge models. The knowledge engineer and development team. Portrait of a knowledge engineer and knowledge manager: psychological and professional profile. Topic 5. Theoretical issues and Practical aspects of KE. Psychological, linguistic and methodological issues. Classification and practice of KE methods. Knowledge structuring techniques. Knowledge Representation Topic 6. Ontological Engineering. Semantic ontology design: step by step. Algorithms and tips for the visual design of ontologies. Ontologies as a kernel of knowledge management. The taxonomy and development of corporate ontologies. Visual tools for ontology development Topic 7. Knowledge Management (KM). Traditional approach and definition. Social and organisational aspects of KM. Cognitive problems of KM. Knowledge sharing techniques. Corporate memory. Corporate knowledge lifecycle. IT-Tools for KM. KM management and company culture.

List of sessions Topic 1. A Brief Introduction to Systems Analysis and Information management Session 1. Issues covered: • Systems, elements, relations, hierarchy. • Information Management: the modern approach. History: a brief synopsis of its evolution. • The main branches of SA and IM. Working with information Objectives for skill development: • Understand the main paradigms of SA • Explain why a systems approach is important • Know main trends of IM • Know main branches of IM • Have basic skills for working with information Assignment for Session 2: • Reading Assignment: Chapter 13 from Glushko (paper 5 from the compendium).

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• •

Tasks and exercises: prepare a well-structured visual systemic map of information you use in your own life. Reading Assignment: paper 6 from the compendium.

Session 2. Issues covered: • A short history. • Knowledge-based systems. • Expert systems. Objectives for skill development: • Understand what a knowledge-based system is • Know the main applications of expert systems Assignment for Session 3: • Reading Assignment: paper 7 from the compendium. • Tasks and exercises: Make INTENSIONAL AND EXTENSIONAL definitions of a concept (bird, book, bus, bag, etc.). Topic 2. Intelligent technologies in IM.

Session 3. Issues covered: • Machine learning. • Data mining and Knowledge discovery Objectives for skill development: • Have a general understanding of ML and DM • Know the market for DM tools Assignment for Session 4: • Reading Assignment: Site by Tony Buzan on mind mapping. • Tasks and exercises: Make a visual draft of computer science history in Visio via a cause-and-effect diagram on the basis of the main facts given in the assignment.

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Session 4 Issues covered: • Knowledge and data. • Practical knowledge structuring: a visual approach. • Mental models. • Mind maps and mind-mapping tools. Objectives for skill development: • Make practical use of mind mapping • Use the Mind Manager and FreeMind software tools Assignment for Session 5: • Reading Assignment: Paper 1. Novak, Joseph D. The Theory Underlying Concept Maps and How To Construct Them, Original material at http://cmap.coginst.uwf.edu/info/printer.html • Tasks and exercises: 1) Draw a mind map of UNIVERSITY. Use Freemind tool to design your visual CV in the form of a mind map. Topic 3. Introduction to Knowledge Engineering (KE) and Visual Approach. Session 5 Issues covered: • Concept maps and tools. • Roadmaps and knowledge maps Objectives for skill development: • Use concept mapping techniques. • Use the Cmap © software tools Assignment for Session 6: • Reading Assignment: Compendium –part 3 of the hand-outs. • Tasks and exercises: create a concept map for the sentence “The EVTEK Company will implement an expensive CRM system for routine daily use by the end of 2008”.

Session 6. Issues covered: • Classification of knowledge models. • The knowledge engineer and development team..

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Objectives for skill development: • Create semantic networks, frames and rule-based models Assignment for Session 7: • Reading Assignment: paper 8 from the compendium. • Tasks and exercises: 1) draw a SEMANTIC NETWORK “Shopping” using the C map tool, write down the RULE-BASED MODEL for “What gift should I bring to a birthday party?” Topic 4. Knowledge representation and practical knowledge engineering. Session 7. Issues covered: • Portrait of a knowledge engineer and knowledge manager: psychological and professional profile.. Objectives for skill development: • Think creatively about and understand the strategic role of knowledge acquisition techniques in information processing and the role of information analysts in this area. Assignment for Session 8: • Reading Assignment: paper 8 from the compendium “Advancement, voluntary turnover and women in IT:A cognitive study of work–family conflict” by Deborah J. Armstrong, Cynthia K. Riemenschneider, Myria W. Allen, Margaret F. Reid • Tasks and exercises: create a concept map “VACATIONS” using C-map tool. Session 8 Issues covered: • A short history. • Knowledge-based systems. • Expert systems. Objectives for skill development: • Recognise any intelligent system • Name the main factors in the effective development of expert systems • Know the lifecycle of the development of intelligent systems

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Assignment for Session 9: • Reading Assignment: paper 9 from the compendium. • Tasks and exercises: find 3 examples of ES in business on the web.

Topic 5. Theoretical issues and Practical aspects of KE. Session 9 Issues covered: • Machine learning. • Data mining and Knowledge discovery Objectives for skill development: • Know the main trends and approaches to DM Assignment for Session 10: • Reading Assignment: paper 10 from the compendium. • Tasks and exercises:1) create DECISION TABLE entitled “What clothes should I wear when going out?”, 2) create a DECISION TREE entitled “Setting up a birthday party”.

Session 10 Issues covered: • Psychological issues in KE, • Linguistic issues in KE • Methodological issues in KE. Objectives for skill development: • Understand the main levels of KE structure • Use methodological and professional tips in KE Assignment for Session 11: • paper 2 from the compendium and paper 6 from the compendium – “A Delphi study of knowledge management systems: Scope and requirements” by Dorit Nevo & Yolande E. Chan • Tasks and exercises: 1)Work out the FRAME for the concept of a “Newspaper”, 2) Extract knowledge from the given text.

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Topic 6. Ontological Engineering. Session 11 Issues covered: • Ontologies as a kernel of knowledge management. • The taxonomy and development of corporate ontologies. • Visual tools for ontology development Objectives for skill development: • Have the skills needed to use visual tools for ontology design and development. • Know the Gestalt principles of good shape. Assignment for Session 12: • Reading Assignment: 1) Any papers on KM by Gomez-Peres or Dieter Fensel. • Tasks and exercises: Create an ontology for the conception of “Management”.

Topic 7. Knowledge Management . Session 12 Issues covered: • Knowledge Management (KM). Social and organisational aspects of KM. Cognitive problems of KM. • Corporate memory. Corporate knowledge lifecycle. • KM management and company culture. Modern examples and case study management etc. • General conclusion of the course I Objectives: • Understand the traditional approach and definition of KM. • Use knowledge-sharing techniques • Have general ideas about IT-Tools for KM and the market for these tools.

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Course reading Required reading Gavrilova, T., Zhukova, S. Knowledge Engineering: learning and application guide. GSOM, 2011 (digital version). Okada, A., Shum, B. S., Sherborne, T. (Eds) Knowledge Cartography: Software Tools and Mapping Techniques (Advanced Information and Knowledge Processing). Springer, 2008. Glushko, R.& McGrath, T. Document Engineering. The Mit Press, 2005. Nast, J. Idea Mapping: How to Access Your Hidden Brain Power, Learn Faster, Remember More, and Achieve Success in Business. Wiley, 2006.

Other required reading Allee, V. The Future of Knowledge: Increasing Prosperity through Value Networks. Butterworth-Heinemann, 2002. Collins, Heidi. Enterprise Knowledge Portals. AMACOM, 2003. Cornelius T. Leondes (Editor). Expert Systems: The Technology of Knowledge Management for the 21st Century Six Volume Set. Academic Press; 1st edition, 2001. Davenport, Thomas H., Laurence Prusak. Working Knowledge. Harvard Business School Press, 2000. Davies, John (Editor), Dieter Fensel (Editor), Frank van Harmelen (Editor). Towards the Semantic Web: Ontology-Driven Knowledge Management. John Wiley & Sons, 2003. Fensel, Dieter. Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce. Springer Verlag, 2001. Gardenfors, Peter. Conceptual Spaces: The Geometry of Thought. MIT Press, 2000. Geroimenko, Vladimir (Editor), Chaomei Chen (Editor). Visualizing the Semantic Web. Springer Verlag, 2003. Gomez-Perez, Asuncion, Corcho, Oscar & Fernandez, Mariano. Ontological Engineering: with examples from the areas of Knowledge Management, eCommerce and the Semantic Web (Advanced Information and Knowledge Processing), Springer, 2008. Joseph M. Firestone Enterprise Information Portals and Knowledge Management. Butterworth-Heinemann, 2002.

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Liebowitz, Jay. Knowledge Management: Learning from Knowledge Engineering. CRC Press, 2001. Loshin, David. Enterprise Knowledge Management: The Data Quality Approach. Morgan Kaufmann, 2001. Milton, N. R. Knowledge Acquisition in Practice: a step-by-step guide. London: Springer. 2007. Nast J. Idea Mapping: How to Access Your Hidden Brain Power, Learn Faster, Remember More, and Achieve Success in Business. Wiley, 2006. Novak, Joseph D. The Theory Underlying Concept Maps and How To Construct Them, Original material at http://cmap.coginst.uwf.edu/info/printer.html Watson, Ian. Applying Knowledge Management: Techniques for Building Corporate Memories. ISB Morgan Kaufmann, 2003.

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Saint-Petersburg State University Graduate School of Management Master’s Program

E-portfolio Course on Knowledge Engineering

Student’s name: Group: E-mail:

2011

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Intensional and Extensional of the concept of a “Bird” (by Elvira Grinberg ) INTENSIONAL definition A bird is an animal which has wings and lays eggs. EXTENSIONAL definition Bird: flamingo, chicken, duck, ostrich

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1. Mind map of “My CV” . Made in Free Mind by Maria Andreeshcheva

A mind map “Student”.

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2. Mind map of the concept of “My CV” and ”Student” Made in Free Mind by Maria Andreeshcheva

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A concept map for “Shopping”. Made in CMap by Ekaterina Konshina.

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A concept map for the sentence “John Brown goes on a short business trip to Moscow by train, in May, with his younger colleague Nick Adams” Made in Cmap by Elvira Grinberg.

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Buy SmartDraw!- purchased copies print this document without a watermark. Visit www.smartdraw.com or call 1-800-768-3729.

A Cause-and-Effect diagram fort the “Evolution of Competitive Intelligence”. Made in Smart Draw by Maria Andreeshcheva.

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3. A Concept map “Exam”. Made in Cmap software by Angelina Merkulova.

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4. A Frame for a concept “Newspaper”. Made in MS Visio by Igor Buryak.

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5. A causal chain entitled “Pass the exam” made in MS Visio (by Igor Buryak).

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6.

A flowchart of “My breakfast preparation” by Igor Buryak.

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Linguistic variable “Salary after first year of work” described by Ekaterina Zykova. Conceptual structure:

Fuzzy sets Not enough: 15,000/1 + 18,000/0.80 +20,000/0.60 + 25,000/0.3 +27,000/0 Small: 18,000/0.6 + 20,000/0.8 + 23,000/0.9 +25,000/1 + 27,000/0.6 + 28,000/0.4 Medium: 27.000/0.6 + 28.000/0.8 + 30.000/ 1+ 35.000/0.8 + 37.000/0.6 + 40 000/0.4 Huge: 35,000/0.5 + 38,000/0.8 + 40,000/0.9 + 45,000/1

Membership function for a medium salary

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7. PEN concept exploration by Igor Buryak. PEN properties 1) 2) 3) 4) 5) 6) 7) 8) 9) 10) 11) 12) 13) 14) 15) 16) 17) 18) 19) 20)

21) Presence of flashlight 22) Presence of precious metals 23) Presence of diamonds 24) Type of grip 25) Material of grip 26) Colour of grip 27) Presence of sticky label of producer 28) Presence of cap 29) Material of cap 30) Colour of cap 31) Presence of vent hole 32) Presence of refillable reservoirs 33) Presence of pencil 34) Presence of stylus 35) Presence of clip 36) Presence of inscription on body 37) Colour of clip 38) Material of clip 39) Presence of knife 40) Presence of laser pointer

Colour of ink Type of ink Material of nib Shape of nib Size of nib Purpose of nib Shape of pen Length of pen Thickness of pen Style of pen Weight of pen Material of body Transparency of body Colour of body Tip size Width of stroke Recyclability Automaticity Strength Disposability

Decision Table: “Which pen to buy” X1

X2

X3

Y

P

User

Price

Presence of devices

Choice

Uncertainty

Pilot Fine

Student Student Businessmen

Low Laser Pointer

Businessmen Soldier Soldier

Knife

Super

Grip

0.5

Universal Living

0.8

Alpec Senator

0.7

Parker Reflex

0.4

Pen Knife

0.9

Erich Krause Maestro

0.3

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Pen conceptual structure

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8. Interviewing an expert on the issue of “Choosing a camera” by Igor Buryak. Interview protocol Total questions: 11 Open questions: 9 Multiple choice questions: 2 Personal: 3 Impersonal: 8 Direct: 9 Indirect: 2

1) Mark the characteristics which do not differ significantly from one model to another: a. Body design b. Price c. Waterproof d. Quantity of pixels e. Zoom f. Size of matrix g. Digital/Film camera 2) Mark the characteristics that you pay the most attention to when choosing a camera: a. Body design b. Price c. Waterproof d. Quantity of pixels e. Zoom f. Size of matrix g. Digital/Film camera 3) What are possible reasons for purchasing a camera? There can be many different reasons for purchasing a camera: for professional shooting and amateur shooting. For example, reporters and wedding photographers use cameras for professional shooting. Examples of amateur shooting are the following: for family events, travelling, to remember fun times with friends… 4) What is the most expensive professional camera segment? Hasselblad, for sure. Any model of this brand.

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5) And what is the most expensive amateur camera? Sony cameras are rather expensive. One of them is the Sony Cyber-Shot DSC-HX1, it is expensive because as it has marvellous characteristics for an amateur camera.

6) And what cameras have beautiful design (both in professional and amateur )? It is difficult to name the most beautiful, but one of the most interesting body designs (among amateur cameras) belongs to the Casio Exilim Ex-G1 model. As for a professional camera, ,that would be the Leica M9.

7) Do you think that characteristic of being waterproof, which you named, is useful? This characteristic is useful if you are going to visit places with transparent water. But if water is muddy, this characteristic is useless. since your photos will be awful anyway. In other words: you should begin by thinking, is it really useful for you, and will you use it frequently?

8) What do most people like about photography? Some people like it for the process of making photos by themselves, as people used to do, by developing film in the bathroom, with red infrared lighting and so on. These people use film cameras. Other people, who do not wish to spend money on film and time on developing their own photos prefer digital cameras.

9) What is an example of a film camera? Zenit.

10) What is an example of a digital camera? There are quite a lot of examples: Canon, Nikon, Olympus, Sony. 11) And which of them would you recommend as the safest one? In my opinion, Canon. Especially, Canon 1D IV.

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Conceptual structure

Decision Table “What photo camera to choose” x1

x2

x3

x4

x5

Professional shooting Professional shooting

Film camera

Professional shooting

Yes

Professional shooting

Yes

Professional shooting

Yes

Amateur shooting Amateur shooting Amateur shooting Amateur shooting Amateur shooting

Film camera Yes Yes Yes

y

Uncertainty

Canon 1D mark IV

0.5

Zenit

0.8

Hasselblad

0.7

Camera Sealife DC 1000

0.8

Leica M9

0.6

Canon PowerShot SX210 IS

0.5

Zenit

0.55

Sony Cyber-Shot DSC-HX1

0.6

Liquid Image HD 321

0.7

Casio Exilim Ex-G1

0.8

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9. Knowledge extraction by Sabine Gueneret from a text on project management.

10. Examples of 3 expert systems by Elina Kaykova. Information was derived from: citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.12.... Fineva - An Expert System for Financial Analysis Of Firms The complete methodology for knowledge acquisition and representation in the field of financial analysis is implemented in the system called FINEVA (FINancial EVAluation). The FINEVA system is a multiple-criteria knowledge-based decision support system for the assessment of corporate performance and viability. The system was developed using the M4 expert system shell, by N.F. Matsatsinis, M. Doumpos and C. Zopounidis of the Technical University of Crete. The output that FINEVA produces is a specific ranking of the firms considered, according to level of risk.

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Port-Man – An Expert System for Portfolio Management In Banks The Port-Man expert system was developed by Y. Y. Chan, T. S. Dillon and E. G. Saw at the La Trobe University in Bundoora, Australia [Chan, Dillon. & Saw 1989]. Port-Man is a banking advisory system designed to assist bank officers in giving advice on personal investment in a bank. It helps to speed up the consultation process and standardise the experience of the bank’s financial consultants. The task of the system is to select a range of bank products that will satisfy the criteria for investment. The selected products are ranked according to rates of return on-investment and risk levels. Moreover, various side-effects for the investor, such as tax variation or pension adjustment, will be taken into consideration. Upon request, the system will give an explanation of how a product is selected. In addition, the user may submit queries to the system during the consultation process. Finally, Port-Man allows the user to change any previous input or investment criteria, and the system will then restart the process at the appropriate stage.

Invex - An Expert System in the Field of Investment Management The INVEX expert system helps the project analyst and investment decision-maker to determine whether a project is acceptable and, if it is, whether it is the best alternative, and to calculate the extent of the decision sensitivity to certain critical assumptions. During a consultation, INVEX first asks about a customer’s preferences and intentions, then builds up a customer profile, where the information asked from customers depends heavily on their intentions and the course of the consultation. These preferences and intentions are translated using production rules into the weights assigned to the different objectives in the multiple-criteria analysis knowledge source. INVEX is fed with data through an interface that most of the users already know; the spreadsheet. MS Excel plays the role of a user-friendly, well-known and well accepted front-end for data entry, a standard financial table generator and translator, and a client of the intelligent server based on the BEST tool for building expert systems performing background intelligent decisionmaking activities.

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11. Taxonomy of “Transportation” by Elina Kaykova.

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12. Partonomy of an “Appartment” by Elina Kaykova.

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13. Partonomy of a “Bag” by Elina Kaykova.

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14. Genealogy of BMW cars by Elina Kaykova.

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Conclusion All the visual models of knowledge are node-link representations, in which ideas are located in nodes and connected to other related ideas through a series of labelled links. The research on knowledge mapping in the last decade has produced a number of consistent and interesting findings (O’Donnell et al., 2002; Dicheva, Dichev, 2007). Students recall more central ideas when they learn from a mind map or ontology than when they learn from text, and those with low verbal ability or low prior knowledge often benefit the most. Learning from maps is enhanced by active processing strategies, such as summarisation or annotation and by designing maps according to Gestalt principles of organisation. Fruitful areas for future research on ontology mapping include examining whether visual representations reduce cognitive load, how map learning is influenced by the structure of the information to be learned, and the possibilities for transfer. In conclusion, we can recommend some creative tips for making different knowledge models (this information comes from http://www.aldridgeshs.eq.edu.au/ sose/mindmaps/tips.htm): • • • • • • • • • • •

Review available visual materials such as photos, sketches, graphs, etc. Focus on a visual language approach to communication. Consider possible formats for visual structuring. Relax, close your eyes and allow your mind to “associate freely”. Draw informal, thumbnail sketches of your visual impressions. Experiment with a variety of visual layout formats. Colour shapes, arrows or words for emphasis. Imagine a bird's eye view of the subject matter to be presented. Look at your work with fresh eyes; is the visual presentation attractive? Ask yourself, are these visuals compelling? Do they help convince the viewer that the subject matter is important and inviting? Integrate the visuals with the text. Does it work well??

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References Adeli, H. (1994). Knowledge Engineering. McGraw-Hill, New-York. Alitek Consulting. (2003), What is strategic mapping?, Available: (Accessed: 2003, August 1) Baader, F., Horrocks I, Sattler U. (2004). Handbook on ontologies, Springer. Booch , G. (2007) Object-Oriented Analysis and Design with Applications (3rd Edition), Addison-Wesley Object Technology Series. Bouguettaya, D. (Editor) (1999). Ontologies and Databases. Kluwer Academic Publishers. Brown, J., A. Collins e P. Duguid, (1989): Situated cognition and the culture of learning, Educational Researcher, vol. 18, n° 1, 32-42 Boose, J.H. (1990). Knowledge Acquisition Tools, Methods and Mediating Representations. In Knowledge Acquisition for Knowledge-Based Systems (Motoda, H. et al., Eds), IOS Press, Ohinsha Ltd., Tokyo, 123-168. Brusilovsky, P. and Rizzo, R. (2002). Map-Based Horizontal Navigation in Educational Hypertext. In Proceedings of Hypertext, University Of Maryland, College Park, USA. Brusilovsky, P., Yudelson, M., Gavrilova, T. (2005). Towards User Modeling Meta Ontology. Lecture Notes in Artificial Intelligence (LNAI 3538) Ed. Ardissono, L., Brna P. & Mitrovic, A. Proceedings of 10th International conference User Modeling UM 2005, Springer, 448-452. Buzan, T. Mind Map handbook. Thorsons, 2005. Chang, S. K. (Editor). (2002). Handbook of Software Engineering and Knowledge Engineering, Vol 1 Fundamentals Vol 2: Emerging Technologies. World Scientific Publishing Co., Inc.,: Conlon, T. (1997). Towards Diversity: Advancing Knowledge-based Modelling with Knowledge Acquisition. In Proceedings of 8th International PEG Conference, Sozopol, Bulgaria., 379-386. Davies, J. (Ed.), Harmelen F. van, Fensel D.(2002). Towards the Semantic Web: Ontology-Driven Knowledge Management.- John Wiley & Sons, Inc. New York, NY, USA Dicheva, D., Aroyo, L. (2004). Concepts and Ontologies in Web-based Educational Systems. Int. J. Cont. Engineering Education and Life-long Learning, 14 (3), 187-190. Dicheva, D., Dichev, C. (2005). Authoring Educational Topic Maps: Can We Make It Easier? Proceedings of ICALT 2005, 216-219.

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