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Taxonomy learning using compound similarity measure

Neshati, M ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1109/WI.2007.99
  3. Publisher: 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 Machine Learning Technique (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. © 2007 IEEE
  6. Keywords:
  7. Arsenic compounds ; Artificial intelligence ; Chlorine compounds ; Education ; Learning algorithms ; Learning systems ; Neural networks ; Ontology ; Cumbersome task ; International conferences ; Machine-learning ; Neural network modelling ; Ontology learning ; Precision and recall ; Similarity measuring ; Web intelligence ; Word similarity ; Taxonomies
  8. Source: IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007, Silicon Valley, CA, 2 November 2007 through 5 November 2007 ; January , 2007 , Pages 487-490 ; 0769530265 (ISBN); 9780769530260 (ISBN)
  9. URL: https://dl.acm.org/doi/abs/10.1109/WI.2007.99