Knowledge Engineering: Learning and Application Guide

133 Pages·2012·4.771 MB·Russian

by  Gavrilova T.A., Zhukova S.V.

St. Petersburg State University
Graduate School of Management

T.A. Gavrilova, S.V. Zhukova

KNOWLEDGE ENGINEERING:
a learning and application guide

St. Petersburg
2012
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 structur-
ing skills are the key to successful knowledge representation and sharing in
any organisation. Students are trained to use Mind Manager and CMap soft-
ware in order to support understanding of highly multidisciplinary horizons of
knowledge engineering. Applications of recent advances in information proc-
essing and cognitive science to management problems are introduced in a va-
riety 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
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

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 dis-
cussion sessions and training, students reveal the tradeoffs of visual knowledge model-
ling, invent and evaluate different alternative methods and solutions for better repre-
sentation 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 en-
gineering, data structuring and cognitive science in information processing and mod-
ern 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 Man-
ager 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, sys-
temic 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;
6 Preface
• 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 map-
ping 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.
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 (elici-
tation, structuring and formalisation) for intelligent (or knowledge-based) systems de-
velopment, 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 engi-
neers have developed a number of principles, methods and tools that have considera-
bly 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 knowl-
edge, 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 repre-
senting 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
8 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 differ-
ent 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 in-
terrelated facts from human experience. The formalisation of knowledge in clear hier-
archies 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 crea-
tion of visual models of knowledge. Visualising techniques make it possible to focus
on the so-called WHAT-knowledge aspects, to arrange and clarify relationships be-
tween 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 ex-
ample of intensional/extensional definitions in Fig. 1.
Chapter 1. Conceptual modelling 9
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 diagram-
ming 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 re-
flected in the process of mind mapping that starts with putting the main concept or prob-
lem 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 or-
ganisation. 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.
10 Chapter 1. Conceptual modelling
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 top-
down 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 nonlin-
ear way, it is proper to use curves instead of straight lines to mimic the nature of think-
ing, 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)