Part-Whole Reasoning in an Object-Centered Framework
by Lambrix, Patrick; Carbonell, J. G.; Siekmann, J.Format: Paperback
Pub. Date: 2000-04-01
Publisher(s): Springer Verlag
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Summary
In this book, the author develops an object-centered framework with specialized support of the part-of relation based on description logics. These logics are a family of object-centered knowledge representation languages tailored for describing knowledge about concepts and is-a hierarchies of these concepts. In addition to the representation and reasoning facilities provided by description logics for is-a, representation and reasoning facilities are introduced for part-of. Finally, the feasibility and the usefulness of the approach is demonstrated by applying the framework to various areas including domain modeling, agent-oriented scenarios, document management and retrieval, and composite concept learning.
Table of Contents
| Background | |
| Introduction | p. 5 |
| Motivation | p. 5 |
| Outline | p. 7 |
| Conventions | p. 9 |
| Description Logics | p. 11 |
| Terminological Knowledge | p. 12 |
| Syntax | p. 12 |
| Semantics | p. 14 |
| Subsumption | p. 15 |
| Assertional Knowledge | p. 17 |
| CLASSIC | p. 18 |
| Composite Objects | p. 21 |
| Composite Objects in Different Areas | p. 22 |
| Mereology | p. 22 |
| Cognitive Science and Linguistics | p. 23 |
| Databases | p. 25 |
| Early Approaches in Knowledge Representation | p. 26 |
| Summary | p. 27 |
| A Basis for Part-Of Representation | p. 28 |
| Mereological Assumptions | p. 28 |
| Scope of the Framework | p. 29 |
| Part-Of in Description Logics | p. 31 |
| General Frameworks | p. 31 |
| A Framework for Physical Whole-Part Relations | p. 31 |
| A Framework with Specialized Role Constructors and Quantifiers | p. 32 |
| A Framework for Part-Of in Engineering Applications | p. 33 |
| Specific Issues | p. 34 |
| Composition Constructor | p. 34 |
| Transitivity Aspects | p. 35 |
| Part-Of in an Object-Based Framework | p. 36 |
| Summary | p. 37 |
| Theory | |
| A Framework for Part-Of Reasoning in Description Logics | p. 41 |
| Introduction | p. 41 |
| Terminological Knowledge | p. 42 |
| Syntax | p. 43 |
| Semantics | p. 45 |
| Subsumption | p. 46 |
| Assertional Knowledge | p. 47 |
| Part-Of Hierarchy for Individuals | p. 50 |
| Implemented Functionality | p. 51 |
| Summary | p. 53 |
| Extending the Framework | p. 55 |
| Introduction | p. 55 |
| Extended Language | p. 56 |
| Terminological Knowledge | p. 56 |
| Assertional Knowledge | p. 65 |
| Compositional Inclusion for Individuals | p. 68 |
| Modules and Compositional Inclusion | p. 68 |
| Modules and Compositional Inclusion for Individuals | p. 72 |
| Compositional Inclusion for Concepts | p. 73 |
| Composes | p. 76 |
| Assembly | p. 81 |
| Compositional Assembly | p. 83 |
| Compositional Extension | p. 83 |
| Algorithm | p. 85 |
| Preference Relation for Compositional Extension | p. 87 |
| Completion | p. 92 |
| Completion | p. 92 |
| Algorithm | p. 93 |
| Preference Relation for Completion | p. 95 |
| Summary | p. 97 |
| Comparison of the Framework with Other Approaches | p. 99 |
| Model for Part-Of | p. 99 |
| Part-Of in the Description Logic | p. 99 |
| Subsumption Relationships | p. 100 |
| Specialized Inferences | p. 101 |
| Part-Of Hierarchy for Individuals | p. 101 |
| Compositional Inclusion Hierarchy for Concepts | p. 101 |
| Application Areas | |
| Domain Modeling in an Agent-Oriented Application | p. 107 |
| Motivation | p. 107 |
| dMARS | p. 109 |
| Reaction Control System | p. 110 |
| Modeling the Belief Knowledge Base | p. 111 |
| Advantages and Disadvantages | p. 115 |
| Advantages | p. 115 |
| Disadvantages | p. 118 |
| Conclusion | p. 119 |
| Document Management | p. 121 |
| Motivation | p. 121 |
| Modeling the Document Management Domain | p. 122 |
| Useful Queries and Inferences | p. 126 |
| Top-Down Instantiation of Documents | p. 126 |
| Bottom-Up Instantiation of Documents | p. 127 |
| Inheritance via Part-Of | p. 127 |
| Recognition of Individuals | p. 128 |
| Retrieval | p. 129 |
| Summary | p. 129 |
| Learning Composite Concepts | p. 131 |
| Motivation | p. 131 |
| Framework | p. 131 |
| Learning Task | p. 132 |
| Useful Operations | p. 133 |
| Least Common Subsumer | p. 133 |
| Specific Concepts | p. 136 |
| Learning Composite Concepts | p. 139 |
| Learning by Using Concepts and Subsumption | p. 139 |
| Learning by Using Individuals | p. 140 |
| Learning by Using Concepts and Part-Of | p. 140 |
| Summary | p. 141 |
| Application | |
| Document Search Using Content, Structure and Properties | p. 145 |
| Model | p. 147 |
| Query Language | p. 147 |
| Document Bases | p. 151 |
| Architecture | p. 151 |
| Knowledge Bases | p. 152 |
| Information Extractor | p. 153 |
| Kernel | p. 153 |
| User Interface | p. 155 |
| Prototype Implementation and Test Results | p. 155 |
| Related Work | p. 163 |
| Conclusion | p. 164 |
| Conclusion | |
| Conclusion | p. 169 |
| Contributions | p. 169 |
| Future Work | p. 171 |
| References | p. 177 |
| Appendices | |
| New User Functions | p. 189 |
| Query Language | p. 193 |
| Symbols | p. 195 |
| Table of Contents provided by Publisher. All Rights Reserved. |
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