The Knowledge Acquisition and Representation Language, KARL

250 Pages·1995·5.512 MB·

by  Dieter Fensel (auth.)

THE KNOWLEDGE ACQUISITION AND
REPRESENTATION LANGUAGE, KARL
THE KNOWLEDGE ACQUISITION AND
REPRESENTATION LANGUAGE, KARL
by
Dieter Fensel
University of Karlsruhe
&
University of Amsterdam
....
"
SPRINGER SCIENCE+BUSINESS MEDIA, LLC
ISBN 978-1-4613-5959-3 ISBN 978-1-4615-2275-1 (eBook)
DOI 10.1007/978-1-4615-2275-1
Library of Congress Cataloging-in-Publication Data
A C.I.P. Catalogue record for this book is available
from the Library of Congress.
Copyright c 1995 by Springer Science+Business Media New York
Originally published by Kluwer Academic Publishers in 1995
Softcover reprint of the hardcover 1s t edition 1995
All rights reserved. No part of this publication may be reproduced, stored in
a retrieval system or transmitted in any form or by any means, mechanical,
photo-copying, recording, or otherwise, without the prior written permission of
the publisher, Springer Science+Business Media, LLC.
Printed on acid-free paper.
FOREWORD
Within the framework of so-called second generation expert systems
[62] knowledge modeling is one of the most important aspects. On the
one hand, knowledge acquisition is no longer seen as a knowledge
transfer process, rather it is now considered as model construction
process which is typically a cyclic and error prone process. On the other
hand, the distinction between knowledge and symbol level descriptions
[166] resulted in various proposals for conceptual knowledge models
describing knowledge in an implementation independent way.
One of the most prominent examples of such a conceptual model is the
KADS model of expertise which is characterized by its clear distinction
of different know ledge types and by the usage of specific modeling
primitives to describe these different knowledge types [185]. The semi
formal KADS expertise model entails all the advantages and
disadvantages which have been identified for semi-formal system
models e.g. in the software engineering community.
Therefore, in the late eighties various research groups started to develop
knowledge modeling languages which aimed at formalizing and/or
operationalizing such an expertise model (see [88] for a detailed
overview and comparison). The development of the Knowledge
Acquisition and Representation Languages KARL, which is completely
specified in this book, is one of the notable efforts to provide such a
modeling language. KARL has been developed as part of the MIKE
project (Model-based and Incremental Knowledge Engineering) [13]
and is characterized by being a formal and operational knowledge
modeling language. Thus, KARL does not only provide a unambiguous
specification of a knowledge based system, but also supports
prototyping in order to meet the requirements of the cyclic and error
prone knowledge modeling process. Since formalization and
operationalization are to some extent competing goals, the development
of KARL gives a lot of insights into the kind of design decisions which
have to be made to achieve both of these goals. Furthermore, these
v
VI FOREWORD
insights are not only valid for the knowledge engineering community,
but also for the software engineering community, since the
development of formal and/or executable specification languages (see
e.g. VDM [114]) bears a lot of common issues with the development of
knowledge modeling languages. Nevertheless, a basic distinction of
knowledge modeling languages like KARL from specification
languages like VDM or Z is that the former languages are based on a
strong conceptual model, e.g. the KADS model of expertise.
When considering KARL from a more technical point of view one can
easily see that a broad knowledge of various computer science areas is
required in order to be able to design such a language. E.g. besides the
KADS model of expertise, KARL is based on work in deductive data
bases (Frame Logic [122]), software engineering (specification
languages), and programming languages (dynamic logic [105]). Thus
KARL is an illustrative example for the kind of interdisciplinary work
which is nowadays required in a lot of research areas to achieve real
progress.
As of spring 1995, the Knowledge Acquisition and Representation
Language KARL is one of the very few knowledge modeling languages
for which a complete formal semantics is defined and for which an
integrated knowledge engineering environment is available. This
environment offers graphical representations of most KARL primitives
in order to enhance the understandability of KARL specifications [87]
and includes an interpreter and debugger for executing KARL
specifications [9]. As such this book considerably advances the state of
the art in the development of knowledge modeling languages.
Karlsruhe, April 1995 Rudi Studer
CONTENTS
LIST OF FIGURES xi
LIST OF TABLES xiii
PREFACE xv
ACKNOWLEDGEMENT xix
1 INTRODUCTION 1
1.1 Model-based and Incremental Knowledge Engineering 1
1.1.1 Model-based Knowledge Engineering 2
1.1.2 Incremental Knowledge Engineering 11
1.1.3 MIKE 14
1.2 The Knowledge Acquisition and Representation
Language KARL 15
1.2.1 A Model of Expertise in KARL 15
1.2.2 Why not using VDM or Z 20
1.2.3 The Formal Semantics 22
1.2.4 The Implementation 22
1.2.5 Case Studies 25
1.3 Some Arguments about Formal and Operational
Specification Languages 28
1.3.1 The Knowledge of Experts Cannot or not
Adequately be Described Formally 32
1.3.2 Formal Languages are Difficult to Learn 32
1.3.3 Formal Specifications are too Complex and too
Difficult to Understand 33
1.3.4 Formal Specifications are too Expensive 36
1.3.5 Should a Specification Language be Executable
or Not 37
vii
viii CONTENTS
2 LOGICAL-KARL 39
2.1 Significant Ideas of Other Approaches Used for
L-KARL 40
2.1.1 Object-orientation and Equality 41
2.1.2 Reasoning About Classes 42
2.1.3 Integration of Well-typing Into a
Model-theoretical Semantics 42
2.1.4 Set-valued Attributes 43
2.1.5 Minimal and Perfect Models as Semantics 43
2.2 Syntax of L-KARL 43
2.3 Informal Semantics of L-KARL 50
2.3.1 IDTerms 51
2.3.2 Class and Predicate Definitions 53
2.3.3 Literals 56
2.3.4 The Difference Between Objects and Values 58
2.3.5 The Well-typing Conditions 59
2.3.6 Formulae 60
2.3.7 Constraints 60
2.4 A Comparison with F-1ogic and O-logic 61
3 PROCEDURAL-KARL 63
3.1 Significant Ideas of Other Approaches Used for
P-KARL 64
3.2 Syntax ofP-KARL 66
3.3 Informal Semantics of P-KARL 69
4 THE KARL MODEL OF EXPERTISE 71
4.1 The Sisyphus Example 72
4.2 The Domain Layer 73
4.2.1 Terminological Knowledge: The Domain
Schema 75
4.2.2 Intensional Descriptions 80
4.2.3 Factual Knowledge 85
CONTENTS ix
4.2.4 Necessary Descriptions 85
4.2.5 Data 87
4.2.6 Graphical Representation 88
4.2.7 The Domain Layer of the Sisyphus Example 89
4.3 The Inference Layer 93
4.3.1 An Alphabet of an Inference Layer 94
4.3.2 Roles 95
4.3.3 Elementary Inference Actions 101
4.3.4 Inference Structure 103
4.3.5 Graphical Representation 107
4.3.6 Inference Structures versus Dataflow Diagrams 110
4.3.7 The Inference Layer of the Sisyphus Example 112
4.4 The Task Layer 122
4.4.1 Language Primitives at the Task Layer 122
4.4.2 Graphical Representation 125
4.4.3 The Task Layer of the Sisyphus Example 126
4.5 The Model of Cooperation 126
4.5.1 Data 129
4.5.2 Control Information 131
5 THE FORMAL SEMANTICS OF KARL 133
5.1 The Formal Semantics of L-KARL 133
5.1.1 Model Theory of L-KARL 134
5.1.2 Herbrand Models 152
5.1.3 Minimal Model Semantics 158
5.1.4 Perfect Model Semantics 166
5.1.5 Constraints 169
5.1.6 Built-in Predicates 170
5.2 The Formal Semantics of P-KARL 172
5.3 The Formal Semantics of a Domain Layer 176
5.4 The Formal Semantics of an Inference Layer 178
5.5 The Formal Semantics of a Task Layer 183
x CONTENTS
6 CONCLUSION 187
6.1 Highlights of KARL 187
6.2 Related Work 189
6.2.1 A Comparison with (ML)2 189
6.2.2 KARL and Structured Analysis 196
6.3 Shortcomings of KARL 202
6.3.1 Current Limitations of KARL 202
6.3.2 Actual Limitations of KARL 205
6.4 Future Work 207
6.4.1 Validation of Conceptual Models 207
6.4.2 Formal Specifications of Reusable Problem-
Solving Methods 208
REFERENCES 213
INDEX 235
LIST OF FIGURES
Chapter 1
1.1 The four-layer model of expertise. 9
1.2 Inference structure of heuristic classification. 10
1.3 MeMoKit tool environment. 24
1.4 Screen dump of the debugger of KARL. 26
1.5 Combining semi-formal and formal specifications. 35
Chapter 4
4.1 The structure of the domain layer. 75
4.2 Graphical representation at the domain layer. 89
4.3 The domain schema of the Sisyphus example. 91
4.4 A typology of roles. 97
4.5 An elementary inference action and its input and output. 102
4.6 Wrong refinements of composed inference actions. 108
4.7 Graphical representation at the inference layer. 109
4.8 Inference structure of test and revise. 110
4.9 Control flow I of test and revise. 110
4.10 Control flow II of test and revise. 111
4.11 Dataflow diagram of test and revise. 111
4.12 Control flow ill of test and revise. 112
4.13 Inference structure of generate-test-select. 113
4.14 The stores designs, correct- designs, and complete
-designs 114
4.15 The view next-component. 115
4.16 The view next-slots. 116
4.17 The view constraints. 116
4.18 The terminator solution. 117
xi