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Ontology Mining for Semantic Interpretation of Information Needs

  • Conference paper
Knowledge Science, Engineering and Management (KSEM 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4798))

Abstract

Ontology is an important technique for semantic interpretation. However, the most existing ontologies are simple computational models based on only “super-” and “sub-class” relationships. In this paper, a computational model is presented for ontology mining, which analyzes the semantic relations of “part-of”, “kind-of” and “related-to”, and interprets the semantics of individual information need. The model is evaluated by comparing the knowledge mined by it, against the knowledge generated manually by linguists. The proposed model enhances Web information gathering from keyword-based to subject(concept)-based. It is a new contribution to knowledge engineering and management.

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Author information

Authors and Affiliations

  1. FIT, Queensland University of Technology, Australia

    Xiaohui Tao, Yuefeng Li & Richi Nayak

Authors
  1. Xiaohui Tao
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  2. Yuefeng Li
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  3. Richi Nayak
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Editor information

Zili Zhang Jörg Siekmann

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Tao, X., Li, Y., Nayak, R. (2007). Ontology Mining for Semantic Interpretation of Information Needs. In: Zhang, Z., Siekmann, J. (eds) Knowledge Science, Engineering and Management. KSEM 2007. Lecture Notes in Computer Science(), vol 4798. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76719-0_32

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  • DOI: https://doi.org/10.1007/978-3-540-76719-0_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76718-3

  • Online ISBN: 978-3-540-76719-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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