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International Conference on Intelligent Text Processing and Computational Linguistics

CICLing 2015: Computational Linguistics and Intelligent Text Processing pp 347–360Cite as

Domain-Specific Semantic Relatedness from Wikipedia Structure: A Case Study in Biomedical Text

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9041)

Abstract

Wikipedia is becoming an important knowledge source in various domain specific applications based on concept representation. This introduces the need for concrete evaluation of Wikipedia as a foundation for computing semantic relatedness between concepts. While lexical resources like WordNet cover generic English well, they are weak in their coverage of domain specific terms and named entities, which is one of the strengths of Wikipedia. Furthermore, semantic relatedness methods that rely on the hierarchical structure of a lexical resource are not directly applicable to the Wikipedia link structure, which is not hierarchical and whose links do not capture well defined semantic relationships like hyponymy.

In this paper we (1) Evaluate Wikipedia in a domain specific semantic relatedness task and demonstrate that Wikipedia based methods can be competitive with state of the art ontology based methods and distributional methods in the biomedical domain (2) Adapt and evaluate the effectiveness of bibliometric methods of various degrees of sophistication on Wikipedia (3) Propose a new graph-based method for calculating semantic relatedness that outperforms existing methods by considering some specific features of Wikipedia structure.

Keywords

  • Semantic Similarity
  • Semantic Relatedness
  • Distributional Method
  • Neighborhood Graph
  • Computational Linguistics

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Authors and Affiliations

  1. Faculty of Computer Science, Dalhousie University, Halifax, NS, Canada, B3H 4R2

    Armin Sajadi, Evangelos E. Milios, Vlado Kešelj & Jeannette C. M. Janssen

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  1. Armin Sajadi
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  2. Evangelos E. Milios
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  3. Vlado Kešelj
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  4. Jeannette C. M. Janssen
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Corresponding author

Correspondence to Armin Sajadi .

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Editors and Affiliations

  1. Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico DF, Mexico

    Alexander Gelbukh

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Sajadi, A., Milios, E.E., Kešelj, V., Janssen, J.C.M. (2015). Domain-Specific Semantic Relatedness from Wikipedia Structure: A Case Study in Biomedical Text. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2015. Lecture Notes in Computer Science(), vol 9041. Springer, Cham. https://doi.org/10.1007/978-3-319-18111-0_26

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  • DOI: https://doi.org/10.1007/978-3-319-18111-0_26

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18110-3

  • Online ISBN: 978-3-319-18111-0

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