Theory and Applications of Ontology: Computer Applications (eBook)

Roberto Poli (B.A. in sociology, with honors, Ph.D. on ontology for knowledge engineers, Utrecht) is editor-in-chief of Axiomathes (Springer), a peer-reviewed academic journal devoted to the study of ontology and cognitive systems, editor of Categories (Ontos), and member of the Academic Board of Directors of the Metanexus Institute, Philadelphia. His research interests include (1) ontology, in both its traditional philosophical understanding and the new, computer-oriented, understanding, (2) the theory of values and the concept of person and (3) anticipatory systems, i.e. system able to take decisions according to their possible future development. Poli has published four books, edited or co-edited more than 20 books or journal’s special issues and published more than 150 scientific papers. He teaches Applied Ethics and Futures Studies at the Faculty of Sociology and gives a course in Ontology at the Faculty of Literature and Philosophy, University of Trento. Michael J. Healy received a B.A degree in mathematics from Eastern Washington University at Cheney, Washington in 1965 and a M.S. in mathematics from the University of Idaho in 1967. From 1967 through 2001 he worked for The Boeing Company, first as a consultant in nonlinear programming and, beginning in 1989, in research. His research spanned neural networks, machine learning, formal verification, and applications of category theory in software synthesis, knowledge based systems (KBS) engineering (KBSE), and knowledge representation and ontologies for KBS interoperability. He is now a Senior Research Scholar in the Electrical and Computer Engineering Department (ECE) at the University of New Mexico, which he joined in 2000. He continues to pursue his research interests in the semantics of neural networks, cognitive neuroscience, knowledge representation, and ontologies for systems, including computational, cognitive, and social systems. Category theory is the central ma thematical discipline for his research, with a particular focus upon categorical logic and model theory.Achilles D. Kameas received his Engineering Diploma (in 1989) and his Ph.D. (in 1995, in Human-Computer Interaction), both from the Department of Computer Engineering and Informatics, Univ. of Patras, Greece. Since 2003, he is an Assistant Professor with the Hellenic Open University, where he teaches software design and engineering. He is also the Director of Research Unit 3 / DAISy (Designing Ambient Intelligent Systems) (http://daisy.cti.gr) at the Research Academic Computer Technology Institute (CTI). Since 2007 he is Deputy Dean of the School of Sciences and Technology (SST) of the Hellenic Open University and Director of the e-Comet Lab (Educational Content, Methodologies and Technologies Lab) (http://eeyem.eap.gr). He has participated as researcher, engineer, group leader or scientific coordinator in several EU R&D projects, such as e-Gadgets, Plants, Social, Astra and Atraco. He has published over 100 journal articles, conference papers and book chapters, authored three university textbooks and co-edited more than five books.He is a member of the Advisory Board of Panorama CA and the scientific committee of the Intelligent Environments conferences; in the past he was elected in the steering boards of the Disappearing Computer network and Convivio network. His current research interests include architectures, languages and tools for ubiquitous computing systems, engineering of ubiquitous computing applications, and engineering and application of ontologies and ontology matching. He is a voting member of IEEE, IEEE CS, ACM and ACM SIGCHI. He is a member of Technical Chamber of Greece, Hellenic AI Society and Hellenic Society for the application of ICT in Education.

Preface 4
Contents 8
Contributors 10
Introduction 13
1 The Interplay Between Ontology as Categorial Analysis and Ontology as Technology 17
1.1 Introduction 17
1.2 Ontology_c 18
1.3 Ontology_t 20
1.3.1 Ontology_t Definitions 20
1.3.2 Ontology_t and Epistemology 21
1.3.3 Ontology_t as Theory with Philosophical Stances 22
1.4 Interplay Between Ontologyc and Ontologyt 23
1.4.1 Developing Formalized Ontologies 24
1.4.2 Ontology, Science, and Levels of Reality 28
1.4.3 Example: An Ontology of Biology 30
1.5 Looking Toward the Future 34
1.5.1 Better Ordering Relations for Ontologies 34
1.5.2 Elaboration of the Distinctions Among Ontology Levels 35
1.5.3 Ontology Modularity, Mapping, and Formalization of Context 36
1.5.4 Representation vs. Reasoning 37
1.5.5 Final Words 38
1.6 Acknowledgments and Disclaimers 38
References 38
2 Ontological Architectures 43
2.1 Introduction 43
2.2 Ontological and Ontology Architecture: Overview 44
2.2.1 Truth and Belief: Ontology, Epistemology, Contextual Semantics, Language, and Applications 44
2.2.2 The Big Picture 45
2.2.3 The Ontology Spectrum 46
2.2.4 The Ontology Maturity Model 51
2.3 Ontological Architecture: Upper, Mid-level, Domain Ontologies 52
2.3.1 What Is an Upper Ontology? 53
2.3.1.1 Upper Ontology Definition 53
2.3.1.2 Upper Ontology vs. Mid-Level Ontology 54
2.3.1.3 Upper Ontology vs. Domain Ontology 54
2.3.2 Why Do We Care About Upper Ontology? 54
2.3.2.1 How Upper Ontologies May Help 54
2.3.2.2 A Software Engineer Analogy 55
2.3.3 What Foundational Ontologies Provide: Ontological Choices 56
2.3.3.1 Descriptive vs. Revisionary 56
2.3.3.2 Multiplicative vs. Reductionist 57
2.3.3.3 Universals, Particulars, Sets, Possible Worlds 57
2.3.3.4 Endurants and Perdurants 59
2.3.4 Upper Ontology Initiatives and Candidates 60
2.4 Structuring the Ontological and Meta-Ontological Space 61
2.4.1 Knowledge Representation Languages and Meta-Ontologies 61
2.4.2 The Lattice of Theories 65
2.4.3 Modularity and Context in the Ontological Space 66
2.4.4 Microtheories, Little Theories, Ontology Versioning 68
2.4.5 Information Flow Framework Meta-Ontology 70
2.5 What the Future Holds: A Vision 72
References 75
3 Organization and Management of Large Categorical Systems 83
3.1 Introduction 83
3.2 Terminological Systems in Medicine 84
3.2.1 Compositionality 85
3.2.2 Navigation 87
3.3 Complex Systems and Modularization in General 88
3.4 Abstract Framework for Modules 89
3.4.1 Overview 90
3.4.2 Formal Preliminaries 91
3.4.3 Defining Modules 91
3.4.4 Example Module Types 94
3.4.4.1 Basic Modules 94
3.4.4.2 Modules in Distributed First Order Logic 95
3.5 Characteristics of Module Notions 95
3.5.1 Informal Characteristics 95
3.5.2 Formal Characteristics 96
3.5.2.1 Characteristics Primarily Based on Either Interfaces, Modules or Systems 96
3.5.2.2 Characteristics with Respect to the Interplay of Modules and Systems 97
3.5.3 Discussion of Characteristics 98
3.6 Analytic Overview of Logical Approaches 99
3.6.1 Conservativity and Disjoint Languages 100
3.6.2 Partition-Based Reasoning 101
3.6.3 Semantic Encapsulation 102
3.6.4 Package-based Description Logics 103
3.6.5 Distributed Logics 103
3.6.6 Summarizing Overview 105
3.7 Concluding Remarks 107
3.7.1 Further Related Areas 107
3.7.2 Conclusions 108
References 109
4 The Information Flow Approach to Ontology-Based Semantic Alignment 117
4.1 Introduction 117
4.2 Ontology-Based Semantic Integration: Basic Concepts and Definitions 118
4.2.1 Semantic Matching 119
4.2.2 Integration Theory 120
4.2.3 Semantic Alignment 121
4.3 Semantic Alignment Through Meaning Coordination 122
4.4 Semantic Alignment Hypotheses 123
4.5 Applications and Explorations 126
4.6 Conclusions 128
References 129
5 Ontological Evaluation and Validation 131
5.1 Introduction 131
5.2 Current Approaches in Ontology Evaluation and Validation 133
5.2.1 Evolution-Based 133
5.2.2 Logical (Rule-Based) 134
5.2.3 Metric-Based (Feature-Based) 135
5.3 OntoQA: Metric-Based Ontology Quality Analysis 137
5.3.1 Schema Metrics 138
5.3.1.1 Relationship Richness 138
5.3.1.2 Inheritance Richness 139
5.3.1.3 Attribute Richness 139
5.3.2 Knowledgebase Metrics 139
5.3.2.1 Class Richness 140
5.3.2.2 Class Connectivity 140
5.3.2.3 Class Importance 140
5.3.2.4 Cohesion 141
5.3.2.5 Relationship Richness 141
5.3.3 OntoQA Results 141
5.4 Conclusion 144
References 144
6 Tools for Ontology Engineering and Management 147
6.1 Introduction 147
6.2 Classification of Ontology Tools 148
6.2.1 Specialized Ontology Engineering Tools 148
6.2.1.1 Ontology Engineering Tools 148
6.2.1.2 Ontologies Combination Tools 152
6.2.1.3 Ontology Management Tools 156
6.2.2 Integrated Ontology Engineering Environments 161
6.3 Selecting the Appropriate Ontology Engineering And Management Tool 163
6.4 Conclusion 165
References 166
7 Ontological Tools: Requirements, Design Issues and Perspectives 171
7.1 Introduction 171
7.2 The Engineering of Ontologies 173
7.2.1 The HCOME Methodology 175
7.2.1.1 Specification Phase 176
7.2.1.2 Conceptualization Phase 176
7.2.1.3 Exploitation Phase 177
7.2.2 The DILIGENT Methodology 177
7.3 Next-Generation Ontology Engineering Tools 179
7.4 Supporting Ontology Engineering 183
7.4.1 Integrated O.E Environments 183
7.4.2 Self-Standing O.E Tools 184
7.5 Conclusion 185
References 188
8 Using the Unified Foundational Ontology (UFO) as a Foundation for General Conceptual Modeling Languages 190
8.1 Introduction 190
8.2 The Unified Foundational Ontology (UFO) 191
8.2.1 The Core Categories: Object--Object Universal, Moment--Moment Universal 191
8.2.2 Qualities, Qualia and Modes 193
8.2.3 Relations, Relators and Qua Individuals 195
8.2.4 Object Universals 198
8.3 A Framework for Language Evaluation and (Re)Design 200
8.4 Evaluating and Redesigning the UML 2.0 Metamodel 203
8.5 Reinforcing the Isomorphism Between UFO and UML 206
8.6 Final Considerations 209
References 210
9 Lightweight Ontologies 212
9.1 Introduction 212
9.2 Lightweight Ontologies 215
9.2.1 Lightweight Ontologies and the Semantic Spectrum 215
9.2.2 Folksonomies and Lightweight Ontologies 218
9.2.3 Thesauri and Lightweight Ontologies 219
9.2.4 Formal Classification and Lightweight Ontologies 219
9.3 Ontologies and the Semantic Web 219
9.4 Ontologies and Information Integration 223
9.5 Ontologies and Knowledge Management 226
9.5.1 Limitations of Current Technology 227
9.5.2 Applying Ontologies in Knowledge Management 229
9.5.3 Semantic Knowledge Management Tools 231
9.5.3.1 Squirrel Semantic Search Engine 231
9.6 Ontologies and Service-Oriented Environments 234
9.6.1 Web Service Modeling Ontology (WSMO) 236
9.6.2 Web Service Modeling Language (WSML) 237
9.6.3 Web Service Modeling Execution Environment (WSMX) 238
9.7 Ontologies and Computer Science 239
9.8 Conclusion 240
References 241
10 WordNet 245
10.1 Introduction 245
10.2 Design and Contents 246
10.3 Coverage 246
10.4 Relations 246
10.5 Nouns in WordNet 247
10.5.1 Hyponymy 247
10.5.2 Types vs. Instances 248
10.5.3 Meronymy 248
10.6 Verbs 248
10.7 Adjectives 249
10.8 Where do Relations Come from? 249
10.9 WordNet as a Thesaurus 250
10.10 Semantic Distance and Lexical Gaps 250
10.11 WordNet as an Ontology 251
10.12 WordNet and Formal Ontology 251
10.13 Wordnets in Other Languages 252
10.14 The EuroWordNet Model 252
10.15 Global WordNets 254
10.16 WordNet as a Tool for Natural Language Processing 254
10.17 Conclusions 255
References 255
11 Controlled English to Logic Translation 258
11.1 Introduction 258
11.2 WordNet Mappings 260
11.3 Simple Parsing and Interpretation 261
11.3.1 Word Sense Disambiguation 262
11.4 Issues in Translation 263
11.4.1 Case Roles and Word Order 263
11.4.2 Statives 264
11.4.3 Attributes 264
11.4.4 Counting 265
11.4.5 Copula Expressions 265
11.4.6 Prepositions 265
11.4.7 Quantification 266
11.4.8 Possessives 267
11.4.9 Anaphor 268
11.4.10 Conjunction and Disjunction 269
11.4.11 Negation 269
11.5 CELT Components 270
References 270
12 Cyc 272
12.1 Introduction 272
12.1.1 The Form of the Language 273
12.1.2 Vocabulary 273
12.1.3 OpenCyc and ResearchCyc 274
12.2 Upper Ontology 275
12.2.1 Higher Order Classes 277
12.3 Contexts 278
12.3.1 Dimensions of Context Space 279
12.3.2 Vocabulary/Theory/Data Contexts 279
12.3.3 Spindles 280
12.3.4 Problem Solving Contexts 281
12.3.5 Hypothetical Contexts 281
12.3.6 Fictional Contexts 281
12.3.7 UniversalVocabularyMt 282
12.4 Functions 282
12.4.1 Prototypes 283
12.4.2 Skolemization 284
12.5 Reasoning 284
12.5.1 Forward and Backward Chaining 284
12.5.2 Don't Care Variables 285
12.5.3 Rule Macro Predicates 285
12.5.4 Monotonic vs. Default Reasoning 286
12.5.5 Exceptions to Rules 286
12.6 Events 287
12.7 Conceptual Works 287
12.8 Open/Closed World Assumption 288
12.9 Geopolitical Entities 288
12.10 Temporal Reasoning 289
12.11 Natural Language Support 289
12.12 Cyc and the Semantic Web 290
12.13 Summary 291
References 291
13 Ontological Foundations of DOLCE 292
13.1 Introduction 292
13.2 A Bit of History 293
13.3 Ontological vs. Conceptual Level 294
13.4 Properties 295
13.5 Basic Categories 298
13.6 Parthood 298
13.7 Time 299
13.8 Temporary Parthood 301
13.9 Concepts 302
13.10 Qualities and Locations 302
13.11 Objects and Events 304
References 308
14 General Formal Ontology (GFO): A Foundational Ontology for Conceptual Modelling 309
14.1 Introduction 309
14.2 Basic Assumptions and Logical Methods 312
14.2.1 Philosophical Assumptions 312
14.2.2 Concepts, Symbols, and Universals 313
14.2.3 The Axiomatic Method 314
14.2.4 Representation of Ontologies 315
14.2.5 Types of Realism 315
14.2.6 Levels of Reality 317
14.3 Meta-Ontological Architecture of GFO 318
14.4 The Basic Categories of Individuals of GFO 319
14.4.1 Space-Time 320
14.4.2 Principal Distinctions 321
14.4.3 Material Structures Material Structure 322
14.4.4 Processual Complexes, Processes, and Occurrents 324
14.4.4.1 Processual Complexes 325
14.4.4.2 Processes 325
14.4.4.3 Occurrents 327
14.4.4.4 Basic Classification of Processes 329
14.4.5 Attributives 331
14.4.5.1 Properties 331
14.4.5.2 Relations and Roles 333
14.4.5.3 Functions 336
14.4.6 Facts, Propositions, and Situations 338
14.5 Basic Relations of GFO 341
14.5.1 Existential Dependency 341
14.5.2 Set and Set-Theoretical Relations 342
14.5.3 Instantiation and Categories 342
14.5.4 Property Relations and Relators 343
14.5.5 Property Bearer Parthood Relation 343
14.5.6 Boundaries, Coincidence, and Adjacence 344
14.5.7 Relations of Concrete Individuals to Space and Time 345
14.5.8 Participation 345
14.5.9 Association 346
14.5.10 Ontical Connectedness and Causality 346
14.6 Object-Process Integration 347
14.6.1 Processual Unification and Cognition 347
14.6.2 Completed Categories and Integrated Individuals 348
14.6.3 Comparison to Other 4D-Ontologies 349
14.7 Principles of Ontology Development and Ontological Modelling 350
14.7.1 Domains and Conceptualizations 350
14.7.2 Steps of Ontology Development 351
14.7.3 Ontological Modelling 353
References 354
15 Ontologies in Biology 358
15.1 Introduction 358
15.2 Ontologies in Biomedicine 360
15.2.1 The Open Biomedical Ontologies 360
15.2.2 The Gene Ontology 363
15.2.3 Ontology Representation 363
15.2.4 Ontology Curation 366
15.2.5 Annotation 366
15.3 Criticism and Extension of the Gene Ontology 368
15.4 Biomedical Ontology Integration Through the Application of Ontological Design Principles 370
15.4.1 The OBO Relationship Ontology 371
15.4.2 BioTop and the Simple Bio Upper Ontology 371
15.4.3 GFO-Bio 372
15.4.4 Defaults and Exceptions for Ontology Interoperability 374
15.5 Applications 376
15.5.1 Annotation and Retrieval of Data 376
15.5.2 Statistical Analysis of Experiments 377
15.5.3 Automatic Annotation and Community-Developed Ontologies 378
15.5.3.1 Automatic Annotation 378
15.5.3.2 Community Development 378
15.5.4 Reasoning for Experimental Hypothesis Testing 379
15.6 Summary and Conclusions 379
References 380
16 The Ontology of Medical Terminological Systems: Towards the Next Generation of Medical Ontologies 383
16.1 Introduction 383
16.2 Terminological Systems and Ontologies 384
16.3 Domains and Graduated Conceptualizations 387
16.4 Analyses of Terminological Systems 389
16.5 Medical Terminological Systems 391
16.5.1 ICD 391
16.5.2 SNOMED-CT 392
16.5.3 UMLS 393
16.5.4 LOINC 394
16.5.5 GALEN 395
16.5.6 MeSH 396
16.6 Conclusions and Future Research 398
References 399
17 Ontologies of Language and Language Processing 402
17.1 Introduction 402
17.2 Lexical Databases and Ontology 405
17.3 Grammatical Motivation and Linguistic Ontology 408
17.4 Discussion 414
References 415
18 Business Ontologies 419
18.1 Introduction 419
18.1.1 Domain-Level Ontologies 420
18.1.2 Application-Level Ontologies 422
18.2 Socio-Instrumental Pragmatism 422
18.2.1 Restructuring the Taxonomy 423
18.2.1.1 Actors 425
18.2.1.2 Objects 425
18.2.1.3 Actions 426
18.2.1.4 Agents 427
18.2.2 The Resulting Meta-model 428
18.3 Enterprise Ontology 428
18.3.1 World Ontology Specification Language 428
18.3.2 The Axioms of Enterprise Ontology 431
18.3.2.1 The Operation Axiom 431
18.3.2.2 The Transaction Axiom 432
18.3.2.3 The Composition Axiom 433
18.3.2.4 The Distinction Axiom 433
18.4 Conclusion 433
References 434
19 Ontologies for E-government 437
19.1 Motivation 437
19.2 State of the Art in E-Government Ontologies 439
19.3 Ontologies to Formalize a Shared Understanding of Meaning 441
19.3.1 Starting with Terms 443
19.3.2 Transforming Terms and Facts to Concepts and Properties 448
19.3.3 Negotiating Reuse 448
19.4 Ontologies for Modelling Semantically Enriched Processes 450
19.5 Ontologies for Modelling Business Rules 453
19.5.1 Business Rules Classification 453
19.5.2 Semi-Formal Rule Respresentation 454
19.5.3 Formalization 456
19.5.3.1 Property Restriction 456
19.5.3.2 Semantic Web Rule Language 457
19.6 Ontologies for Modelling Agile E-Government Processes A process is considered agile when its execution model is created flexible at runtime, based on the results of triggered rules instead of static pre-defined models. 463
19.7 Conclusion 467
References 468
20 An Ontology-Driven Approach and a Context Management Framework for Ubiquitous Computing Applications 471
20.1 Introduction 471
20.2 Ontology Based Modeling of Context Aware Ubiquitous Computing Systems 472
20.3 An Ontology-Driven Meta-Model for Ubiquitous Computing Systems 475
20.3.1 Underlying Concepts 475
20.3.2 Focused Ontology 477
20.3.3 Core vs. Application Ontology 479
20.4 Context Management Framework 480
20.4.1 Context Management Process 480
20.4.2 Rules 481
20.4.2.1 Rules for Artifact State Assessment 482
20.4.2.2 Rules for the Local Decision-Making Process 482
20.4.2.3 Rules for the Global Decision-Making Process 482
20.4.3 Implementation 482
20.4.4 Engineering Applications 484
20.5 Prototype Application Example 485
20.5.1 Scenario 485
20.5.2 Components 486
20.5.3 Implementation 486
20.5.4 Semantic-Based Service Discovery 489
20.6 Conclusions 491
References 491
21 Category Theory as a Mathematics for Formalizing Ontologies 494
21.1 Introduction 494
21.2 Categories 497
21.3 Limits, Colimits, and Concepts as Theories 501
21.4 Structural Mappings 506
21.5 Categories of Categories, Functors, and Natural Transformations 509
21.6 Universal Arrows and Adjunctions 512
References 515
22 Issues of Logic, Algebra and Topology in Ontology 518
22.1 Introduction 518
22.2 Ingredients of Logic 520
22.2.1 Interpretations and Ontology 523
22.2.2 Theories and Models 524
22.3 Geometric Logic 525
22.3.1 Rules of Inference 526
22.3.2 Soundness 528
22.3.3 Beyond Rules of Inference 529
22.3.4 Geometric Ontology 530
22.4 Topology 533
22.5 Algebra 533
22.5.1 Lists and Finite Sets 534
22.5.2 Free Algebras 535
22.6 Categories 536
22.6.1 Sheaves 537
References 538
23 The Institutional Approach 539
23.1 Introduction 539
23.1.1 Ontologies 541
23.1.2 Semantic Integration 543
23.1.3 Architecture 545
23.2 Contexts 548
23.2.1 General Theory 548
23.2.2 Special Theory 551
23.3 Indexed Contexts 553
23.3.1 General Theory 553
23.3.2 Special Theory 555
23.4 Diagrams 557
23.4.1 General Theory 557
23.4.2 Special Theory 559
23.5 Coalescence 564
23.6 Fusion 564
23.6.1 General Theory 564
23.6.2 Special Theory 566
23.7 Formalism 568
References 569
24 Ontology Engineering, Universal Algebra, and Category Theory 570
24.1 Introduction 570
24.2 Representing Ontologies 571
24.3 Presenting Ontologies 573
24.4 Views Versus Sub-Ontologies 575
24.5 Interoperations 575
24.6 Solving View Updates 577
24.7 Interoperations with Instances 578
24.8 Nulls and Partial Functions 579
24.9 Universal Nulls 580
24.10 Conclusion 580
References 581