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

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Springer Science & Business Media, Dec 11, 2006 - Computers - 347 pages
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In the last decade, ontologies have received much attention within computer science and related disciplines, most often as the semantic web. Ontology Learning and Population from Text: Algorithms, Evaluation and Applications discusses ontologies for the semantic web, as well as knowledge management, information retrieval, text clustering and classification, as well as natural language processing.

Ontology Learning and Population from Text: Algorithms, Evaluation and Applications is structured for research scientists and practitioners in industry. This book is also suitable for graduate-level students in computer science.

 

 

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Contents

Introduction
4
Ontologies
9
Ontology Learning from Text
19
31 Ontology Learning Tasks
23
312 Synonyms
24
314 Concept Hierarchies
25
316 Axiom Schemata Instantiations
26
33 The StateoftheArt
27
714 Road Map
187
72 Learning Attributes
188
721 Approach
191
722 Results
192
723 Summary
198
73 Learning Relations from Corpora
199
732 Generalizing Verb Frames
200
734 Evaluation
204

333 Concepts
28
335 Relations
29
336 Axiom Schemata Instantiation and General Axioms
30
34 Contribution and Scope of the Book
31
Basics
35
411 Preprocessing
37
Chunking
39
Parsing
40
414 Contextual Features
41
415 Similarity and the Vector Space Model
44
416 Hypothesis Testing
50
417 Term Relevance
52
418 WordNet
54
42 Formal Concept Analysis
56
43 Machine Learning
62
432 Unsupervised Learning
69
Datasets
76
513 British National Corpus
78
516 OHSUMED
79
522 Genia
80
523 MeSH
81
Concept Hierarchy Induction
83
61 Common Approaches
86
612 LexicoSyntactic Patterns
88
613 Distributional Similarity
90
614 Cooccurrence Analysis
92
615 Road Map
93
62 Learning Concept Hierarchies with FCA
94
621 FCA for Concept Hierarchy Induction
95
622 Context Construction
99
623 Evaluation
102
624 Results
110
625 Summary
122
63 Guided Clustering
123
631 OracleGuided Agglomerative Clustering
124
632 Evaluation
132
633 Summary
141
64 Learning from Heterogeneous Sources of Evidence
142
641 Heterogeneous Sources of Evidence
143
642 Evaluation
149
643 Summary
154
65 Related Work
156
652 Taxonomy Refinement and Extension
174
66 Conclusion and Open Issues
182
Learning Attributes and Relations
185
712 Syntactic Dependencies
186
735 Summary
206
74 Learning Qualia Structures from the Web
207
741 Qualia Structures
208
742 Approach
209
743 Evaluation
214
744 Summary
221
75 Related Work
222
76 Conclusion and Open Issues
230
Population
233
81 Common Approaches
234
813 Supervised Approaches
235
814 Knowledgebased and Linguistic Approaches
237
82 Corpusbased Population
238
821 Similaritybased Classification of Named Entities
239
822 Evaluation
240
823 Experiments
241
824 Summary
249
831 PANKOW
252
832 CPANKOW
260
833 Summary
271
84 Related Work
274
85 Conclusion and Open Issues
279
Applications
281
91 Text Clustering and Classification
283
911 Building Hierarchies
284
912 Conceptual Document Representations
285
913 Experiments
286
914 Summary
290
92 Information Highlighting for Supporting Search
292
922 Experimental Settings
293
923 Results
296
924 Summary
298
93 Related Work
299
932 Information Retrieval
300
933 Text Clustering and Classification
301
94 Contribution and Open Issues
304
Contribution and Outlook
309
Concluding Remarks
311
Appendix
313
A2 Mutually Similar Words for the tourism domain
317
A3 Mutually Similar Words for the finance domain
318
A4 The Penn Treebank Tag Set
320
References
321
Index
344
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