Abstract
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.
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Neshati, M., Hassanabadi, L.S. (2007). Taxonomy Construction Using Compound Similarity Measure. In: Meersman, R., Tari, Z. (eds) On the Move to Meaningful Internet Systems 2007: CoopIS, DOA, ODBASE, GADA, and IS. OTM 2007. Lecture Notes in Computer Science, vol 4803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76848-7_61
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DOI: https://doi.org/10.1007/978-3-540-76848-7_61
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