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Learning Concepts and Taxonomic Relations by Metric Learning for Regression

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  • Chen Sun
  • Ming Zhao
  • Yangjing Long

Abstract

Ontology learning has become a major area of research within the current ontology engineering. The acquisition of concepts and taxonomic relations is one of the key issues of ontology learning. There are various taxonomic ways for the same domain and the related concepts. However, current ontology learning tools have not considered how to generate the most suitable taxonomy for the specific application. In this article, we present a new supervised approach for taxonomic relationship learning. Taking the Chinese unstructured text on the Web as the corpus and a certain particular domain application as the guide, this method uses a given branch of the target ontology and integrates multi-strategy which includes shallow syntactic analysis and kinds of statistical measures to extract concepts and to determine the concepts’ hierarchy numbers and is-a relations. The experiments using a working prototype of the system revealed the improved performance of domain ontology taxonomic relations learning.

Suggested Citation

  • Chen Sun & Ming Zhao & Yangjing Long, 2014. "Learning Concepts and Taxonomic Relations by Metric Learning for Regression," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(14), pages 2938-2950, July.
    • Handle: RePEc:taf:lstaxx:v:43:y:2014:i:14:p:2938-2950
    DOI: 10.1080/03610926.2012.690487
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