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Automatic extraction of is-a relations in taxonomy learning

Neshati, M ; Sharif University of Technology | 2008

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-540-89985-3_3
  3. Publisher: 2008
  4. Abstract:
  5. Taxonomy learning is a prerequisite step for ontology learning. In order to create a taxonomy, first of all, existing 'is-a' relations between words should be extracted. A known way to extract 'is-a' relations is finding lexicosyntactic patterns in large text corpus. Although this approach produces results with high precision but it suffers from low values of recall. Furthermore developing a comprehensive set of patterns is tedious and cumbersome. In this paper, firstly, we introduce an approach for developing lexico-syntactic patterns automatically using the snippets of search engine results and then, challenge the law recall of this approach using a combined model, which is based on cooccurrence of pair words in the web and neural network classifier. Using our approach both precision and recall of extracted 'is-a' relations improved and FMeasure value reaches 0.72. © 2008 Springer-Verlag
  6. Keywords:
  7. Automatic extraction ; Co-occurrence ; Combined model ; F-measure ; High precision ; Lexico-syntactic patterns ; Neural network classifier ; Ontology engineering ; Ontology learning ; Precision and recall ; Search engine results ; Text corpora ; Engineering education ; Neural networks ; Ontology ; Search engines ; Taxonomies ; Semantic web
  8. Source: 13th International Computer Society of Iran Computer Conference on Advances in Computer Science and Engineering, CSICC 2008, Kish Island, 9 March 2008 through 11 March 2008 ; Volume 6 CCIS , 2008 , Pages 17-24 ; 18650929 (ISSN); 3540899847 (ISBN); 9783540899846 (ISBN)
  9. URL: https://link.springer.com/chapter/10.1007/978-3-540-89985-3_3