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Taxonomy construction using compound similarity measure

Neshati, M ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-540-76848-7_61
  3. Publisher: Springer Verlag , 2007
  4. Abstract:
  5. Taxonomy learning is one of the major steps in ontology learning process. Manual construction of taxonomies is a time-consuming and cumbersome task. Recently many researchers have focused on automatic taxonomy learning, but still quality of generated taxonomies is not satisfactory. In this paper we have proposed a new compound similarity measure. This measure is based on both knowledge poor and knowledge rich approaches to find word similarity. We also used Neural Network model for combination of several similarity methods. We have compared our method with simple syntactic similarity measure. Our measure considerably improves the precision and recall of automatic generated taxonomies. © Springer-Verlag Berlin Heidelberg 2007
  6. Keywords:
  7. Knowledge engineering ; Learning systems ; Mathematical models ; Neural networks ; Manual construction ; Ontology learning ; Syntactic similarity measures ; Taxonomies
  8. Source: OTM Confederated International Conferences CoopIS, DOA, ODBASE, GADA, and IS 2007, Vilamoura, 25 November 2007 through 30 November 2007 ; Volume 4803 LNCS, Issue PART 1 , 2007 , Pages 915-932 ; 03029743 (ISSN); 9783540768463 (ISBN)
  9. URL: https://link.springer.com/chapter/10.1007%2F978-3-540-76848-7_61