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本书是"计算语言学与语言科技原文丛书"中的一册。本书是全面论述本体知识库、词汇库以及两者界面建构的本专著，内容涵盖理论和实践两个方面的研究成果，对研究语言语义学、计算语言学和通过自然语言处理进行知识挖据以及本体建构的研究人员有重要参考价值。

导读 1

Contributors 17

Preface 23

Part I Fundamental aspects 1

1 Ontology and the lexicon： a multidisciplinary perspective 3

1.1 Situating ontologies and lexical resources 3

1.2 The content of ontologies 10

1.3 Theoretical framework for the

ontologies／lexicons interface 14

1.4 From ontologies to the lexicon and back 21

1.5 Outline of chapters 23

2 Formal ontology as interlingua： the SUMO and

WordNet linking project and global WordNet 25

2.1 WordNet 25

2.2 Principles of construction of formal ontologies

and lexicons 29

2.3 Mappings 30

2.4 Interpreting language 32

2.5 Global WordNet 33

2.6 SUMO translation templates 35

3 Interfacing WordNet with DOLCE： towards OntoWordNet 36

3.1 Introduction 36

3.2 WordNet’s preliminary analysis 37

3.3 The DOLCE upper ontology 39

3.4 Mapping WordNet into DOLCE 48

3.5 Conclusion 52

4 Reasoning over natural language text by means of FrameNet and ontologies 53

4.1 Introduction 53

4.2 An introduction to the FrameNet lexicon 54

4.3 Linking FrameNet to ontologies for reasoning 56

4.4 Formalizing FrameNet in OWL DL 57

4.5 Reasoning over FrameNet-annotated text 62

4.6 Linking FrameNet to SUMO 66

4.7 Discussion 69

4.8 Conclusion and outlook 70

5 Synergizing ontologies and the lexicon： a roadmap 72

5.1 Formal mappings between ontologies 72

5.2 Evaluation of ontolex resources 73

5.3 Bridging different lexical models and resources 75

5.4 Technological framework 77

Part II Discovery and representation of conceptual systems 79

6 Experiments of ontology construction with Formal Concept Analysis 81

6.1 Introduction 81

6.2 Basic concepts and related work 82

6.3 Dataset selection and design of experiments 86

6.4 Evaluation and discussion 92

6.5 Conclusion and future work 96

7 Ontology， lexicon， and fact repository as leveraged to interpret events of change 98

7.1 Introduction 98

7.2 A snapshot of OntoSem 100

7.3 Motivation for pursuing deep analysis of events of change 101

7.4 Increase 102

7.5 Content divorced from its rendering 114

7.6 NLP with reasoning and for reasoning 117

7.7 Conclusion 118

8 Hantology： conceptual system discovery based on orthographic convention 122

8.1 Introduction： hanzi and conventionalized conceptualization 122

8.2 General framework 126

8.3 Conceptualization and classification of the radicals system 128

8.4 The ontology of a radical as a semantic symbol 132

8.5 The architecture of Hantology 133

8.6 OWL encoding of Hantology 137

8.7 Summary 139

8.8 Conclusion 142

9 What’s in a schema？ 144

9.1 Introduction 144

9.2 An ontology for cognitive linguistics 146

9.3 The c.DnS ontology 148

9.4 Schemata， mental spaces， and constructions 161

9.5 An embodied semiotic metamodel 166

9.6 Applying Semion to FrameNet and related resources 169

9.7 Conclusion 181

Part III Interfacing ontologies and lexical resources 183

10 Interfacing ontologies and lexical resources 185

10.1 Introduction 185

10.2 Classifying experiments in ontologies and lexical resources 185

10.3 Ontologies and their construction 188

10.4 How actual resources fit the classification 190

10.5 Two practical examples 194

10.6 Available tools for the ontology lexical resource interface 196

10.7 Conclusion 200

11 Sinica BOW （Bilingual Ontological WordNet）：integration of bilingual WordNet and SUMO 201

11.1 Background and motivation 201

11.2 Resources and structure required in the BOW approach 202

11.3 Interfacing multiple resources： a lexicon-driven approach 204

11.4 Integration of multiple knowledge sources 207

11.5 Updating and future improvements 209

11.6 Conclusion 210

12 Ontology-based semantic lexicons：mapping between terms and object deions 212

12.1 Introduction 212

12.2 Why we need semantic lexicons 213

12.3 More semantics than we need 215

12.4 The semantics we need is in ontologies 218

12.5 Conclusion 223

13 Merging global and speized linguistic ontologies 224

13.1 Introduction 224

13.2 Linguistic ontologies versus formal ontologies 226

13.3 Speized linguistic ontologies 229

13.4 The plug-in approach 230

13.5 Experiments 236

13.6 Applications and extensions 237

13.7 Conclusion 238

Part IV Learning and using ontological knowledge 239

14 The life cycle of knowledge 241

14.1 Introduction 241

14.2 Using ontolexical knowledge in NLP 242

14.3 Creating ontolexical knowledge with NLP 249

14.4 Conclusion 256

15 The Omega ontology 258

15.1 Introduction 258

15.2 Constituents of Omega 258

15.3 Structure of Omega 260

15.4 Construction of Omega via merging 263

15.5 Omega’s auxiliary knowledge sources 264

15.6 Applications 266

15.7 Omega 5 and the OntoNotes project 267

15.8 Discussion and future work 268

15.9 Conclusion 269

16 Automatic acquisition of lexico-semantic knowledge for question answering 271

16.1 Introduction 271

16.2 Lexico-semantic knowledge for QA 272

16.3 Related work 274

16.4 Extracting semantically similar words 275

16.5 Using automatically acquired role and function words 279

16.6 Using automatically acquired categorized NEs 280

16.7 Evaluation 283

16.8 Conclusion and future work 286

17 Agricultural ontology construction and maintenance in Thai 288

17.1 Introduction 288

17.2 A framework of ontology construction and maintenance 290

17.3 Ontology acquisition from texts 291

17.4 Ontology acquisitions from a dictionary and a thesaurus 301

17.5 Integration into an ontological tree 306

17.6 Conclusion 307

References 309

Index 335

Chu-Ren Huang，香港理工大学教授，文学院院长。

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    There are also works which take words or terms as objects.In Cimiano et al,2003, verb-object dependencies were extracted from texts where the headword of a grammatical object was considered an FCA object and the corresponding verb together with the postfix 'able' were used as attributes, In Marek et al,2004, only adjectives were chosen as attributes because for the chosen set of object terms, such as river, lake, pond, etc., adjectives are more discriminating.The ability of attributes to distinguish between objects, for example, because they exhibit certain word-type preferences, is a basic criterion for choosing them as attributes.Priss (2005) gave a good summary on the linguistic appli- cation of FCA and hypothesized that lexical databases could be represented or analysed using FCA.

6.3 Dataset selection and design of experiments.

The construction of an ontology usmg FCA involves first identifying terms associated with certain concepts to serve as formal objects in FCA and then identifying the most appropriate set of attributes to describe these objects.The relations between concepts can then be extracted through the FCA con- cept lattice.If the concepts are predetermined, the most inportant issue is the selection of an appfopriate attribute set.Of course, the selection of attributes is dependent on the data source used.

Generally speaking, data sources are either lexical or corpus based.The advantage of using a lexical base is that each t.erm already exists and can be easily matched to an attribute set compiled by experts.The advantage of a corpus-based approach is that it is free of human bias because attributes are selected based on real data.However, the quality of the corpus-based approach depends on the availability of a representative large-scale domain corpus.

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编辑推荐

《本体与词汇库——自然语言处理角度的解析（英文影印版）》关注如何整合词典资源和语义手段，内容涵盖理论和实践两个方面的研究成果，适用于对自然语言处理、计算语言学、心理语言学等感兴趣的研究者。

书籍介绍

《本体与词汇库——自然语言处理角度的解析（英文影印版）》是"计算语言学与语言科技原文丛书"中的一册。本书是全面论述本体知识库、词汇库以及两者界面建构的第一本专著，内容涵盖理论和实践两个方面的研究成果，对研究语言语义学、计算语言学和通过自然语言处理进行知识挖据以及本体建构的研究人员有重要参考价值。