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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXII pp 145–164Cite as

Hypothesis Discovery Exploiting Closed Chains of Relations

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Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 9430))

Abstract

The ever-growing literature in biomedicine makes it virtually impossible for individuals to grasp all the information relevant to their interests. Since even experts’ knowledge is limited, important associations among key biomedical concepts may remain unnoticed in the flood of information. Discovering those hidden associations is called hypothesis discovery or literature-based discovery. This paper propose an approach to this problem taking advantage of a closed, triangular chain of relations extracted from the existing literature. We consider such chains of relations as implicit rules to generate explanatory hypotheses. The hypotheses generated from the implicit rules are then compared with newer knowledge for assessing their validity and, if validated, they are served as positive examples for learning a regression model to rank hypotheses. As a proof of concept, the proposed framework is empirically evaluated on real-world knowledge extracted from the biomedical literature. The results demonstrate that the framework is able to produce legitimate hypotheses and that the proposed ranking approach is more effective than the previous work.

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Notes

  1. 1.

    http://www.ncbi.nlm.nih.gov/entrez.

  2. 2.

    http://www.nlm.nih.gov/research/umls/.

  3. 3.

    http://www.nlm.nih.gov/mesh/.

  4. 4.

    http://semanticnetwork.nlm.nih.gov/.

  5. 5.

    In fact, three relations, “actinomycin D inhibits mRNA”, “mRNA directs protein synthesis”, and “actinomycin D impairs protein synthesis”, were extracted from Medline, and this rule was acquired without manually coding any domain knowledge.

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Acknowledgements

This work is partially supported by JSPS KAKENHI Grant Numbers 25330363 and MEXT, Japan.

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Authors and Affiliations

  1. Konan University, Kobe, Hyogo, 658-8501, Japan

    Kazuhiro Seki

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  1. Kazuhiro Seki
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Correspondence to Kazuhiro Seki .

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Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz, Linz, Austria

    Josef Küng

  3. FAW, University of Linz, Linz, Austria

    Roland Wagner

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Seki, K. (2015). Hypothesis Discovery Exploiting Closed Chains of Relations. In: Hameurlain, A., Küng, J., Wagner, R. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXII. Lecture Notes in Computer Science(), vol 9430. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48567-5_5

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  • DOI: https://doi.org/10.1007/978-3-662-48567-5_5

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