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Literature-Based Knowledge Discovery from Relationship Associations Based on a DL Ontology Created from MeSH

  • Conference paper
Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2010)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 272))

Abstract

Literature-based knowledge discovery generates potential discoveries from associations between specific concepts that have been previously reported in the literature. However, because the associations are generally between individual concepts, the knowledge of specific relationships between those concepts is lost. A description logic (DL) ontology adds a set of logically defined relationship types, called properties, to a classification of concepts for a particular knowledge domain. Properties can represent specific relationships between instances of concepts used to describe the things studied by a particular researcher. These relationships form a “triple” consisting of a domain instance, a range instance, and the property specifying the way those instances are related. A “relationship association” is a pair of relationship triples where one of the instances from each relationship can be determined to be semantically equivalent. In this paper, we report our work to structure a subset of more than 1300 terms from the Medical Subject Headings (MeSH) controlled vocabulary into a DL ontology, and to use that DL ontology to create a corpus of A-Boxes, which we call “semantic statements”, each of which describes one of 392 research articles that we selected from MEDLINE. Relationship associations were extracted from the corpus of semantic statements using a previously reported technique. Then, by making the assumption of the transitivity of association used in literature-based knowledge discovery, we generate hypothetical relationship associations by combining pairs of relationship associations. We then evaluate the “interestingness” of those candidate knowledge discoveries from a life science perspective.

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Author information

Authors and Affiliations

  1. Future Center Initiative, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba, 277-8563, Japan

    Steven B. Kraines & Weisen Guo

  2. Springer Japan KK, 3-8-1 Nishi-Kanda, Chiyoda-ku, Tokyo, 101-0065, Japan

    Daisuke Hoshiyama

  3. Institute of Industrial Sciences, University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan

    Takaki Makino

  4. Department of Molecular and Cellular Biology, Harvard University, 52 Oxford St, Cambridge, MA, 02138, USA

    Haruo Mizutani

  5. NalaPro Technologies, Inc., 4-12-16 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan

    Yoshihiro Okuda & Yo Shidahara

  6. Department of Bioinformatics, School of Frontier Science, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba, 277-8568, Japan

    Toshihisa Takagi

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  1. Steven B. Kraines
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Editor information

Editors and Affiliations

  1. IST - Technical University of Lisbon, Av.Rovisco Pais, 1, 1049-001, Lisbon, Portugal

    Ana Fred

  2. Delft University of Technology, Mekelweg 4, 2628 CD, Delft, The Netherlands

    Jan L. G. Dietz

  3. Informatics Research Centre, University of Reading, UK

    Kecheng Liu

  4. Departament of Systems and Informatics, Polytechnic Institute of Setúbal – INSTICC, Rua do Vale de Chaves - Estefanilha, 2910-761, Setúbal, Portugal

    Joaquim Filipe

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Kraines, S.B. et al. (2013). Literature-Based Knowledge Discovery from Relationship Associations Based on a DL Ontology Created from MeSH. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2010. Communications in Computer and Information Science, vol 272. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29764-9_6

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  • DOI: https://doi.org/10.1007/978-3-642-29764-9_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29763-2

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