Ontology Learning and Population from Text - Algorithms, Evaluation and Applications

Ontology Learning and Population from Text - Algorithms, Evaluation and Applications

von: Philipp Cimiano

Springer-Verlag, 2006

ISBN: 9780387392523

Sprache: Englisch

362 Seiten, Download: 19046 KB

 
Format:  PDF, auch als Online-Lesen

geeignet für: Apple iPad, Android Tablet PC's Online-Lesen PC, MAC, Laptop


 

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Ontology Learning and Population from Text - Algorithms, Evaluation and Applications



  Contents 7  
  List of Figures 11  
  List of Tables 13  
  Foreword 15  
  Preface 17  
  Acknowledgements 21  
  Abbreviations 25  
  Mathematical Notation 27  
  Part I Preliminaries 29  
     Introduction 30  
     Ontologies 35  
     Ontology Learning from Text 44  
        3.1 Ontology Learning Tasks 48  
     Basics 60  
        4.1 Natural Language Processing 60  
        4.2 Formal Concept Analysis 81  
        4.3 Machine Learning 87  
     Datasets 101  
        5.1 Corpora 101  
        5.2 Concept Hierarchies 103  
        5.3 Population Gold Standard 105  
  Part II Methods and Applications 106  
     Concept Hierarchy Induction 107  
        6.1 Common Approaches 108  
        6.2 Learning Concept Hierarchies with FCA 116  
        6.3 Guided Clustering 145  
        6.4 Learning from Heterogeneous Sources of Evidence 164  
        6.5 Related Work 178  
        6.6 Conclusion and Open Issues 204  
     Learning Attributes and Relations 207  
        7.1 Common Approaches 207  
        7.2 Learning Attributes 210  
        7.3 Learning Relations from Corpora 221  
        7.4 Learning Qualia Structures from the Web 229  
        7.5 Related Work 244  
        7.6 Conclusion and Open Issues 252  
     Population 254  
        8.1 Common Approaches 255  
        8.2 Corpus-based Population 259  
        8.3 Learning by Googling 270  
        8.4 Related Work 295  
        8.5 Conclusion and Open Issues 300  
     Applications 302  
        9.1 Text Clustering and Classification 304  
        9.2 Information Highlighting for Supporting Search 313  
        9.3 Related Work 320  
        9.4 Contribution and Open Issues 325  
  Part III Conclusion 327  
     Contribution and Outlook 328  
     Concluding Remarks 330  
  Appendix 332  
     A. l Learning Accuracy 332  
     A.2 Mutually Similar Words for the tourism domain 336  
     A.3 Mutually Similar Words for the finance domain 337  
     A.4 The Penn Treebank Tag Set 339  
  References 340  
  Index 363  

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