IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v9y2015i4p686-703.html
   My bibliography  Save this article

SemPathFinder: Semantic path analysis for discovering publicly unknown knowledge

Author

Listed:
  • Song, Min
  • Heo, Go Eun
  • Ding, Ying

Abstract

The enormous amount of biomedicine's natural-language texts creates a daunting challenge to discover novel and interesting patterns embedded in the text corpora that help biomedical professionals find new drugs and treatments. These patterns constitute entities such as genes, compounds, treatments, and side effects and their associations that spread across publications in different biomedical specialties. This paper proposes SemPathFinder to discover previously unknown relations in biomedical text. SemPathFinder overcomes the problems of Swanson's ABC model by using semantic path analysis to tell a story about plausible connections between biological terms. Storytelling-based semantic path analysis can be viewed as relation navigation for bio-entities that are semantically close to each other, and reveals insight into how a series of entity pairs is organized, and how it can be harnessed to explain seemingly unrelated connections. We apply SemPathFinder for two well-known use cases of Swanson's ABC model, and the experimental results show that SemPathFinder detects all intermediate terms except for one and also infers several interesting new hypotheses.

Suggested Citation

  • Song, Min & Heo, Go Eun & Ding, Ying, 2015. "SemPathFinder: Semantic path analysis for discovering publicly unknown knowledge," Journal of Informetrics, Elsevier, vol. 9(4), pages 686-703.
    • Handle: RePEc:eee:infome:v:9:y:2015:i:4:p:686-703
    DOI: 10.1016/j.joi.2015.06.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157715200533
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.joi.2015.06.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mikhail V. Blagosklonny & Arthur B. Pardee, 2002. "Conceptual biology: Unearthing the gems," Nature, Nature, vol. 416(6879), pages 373-373, March.
    2. Don R. Swanson, 1989. "A second example of mutually isolated medical literatures related by implicit, unnoticed connections," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 40(6), pages 432-435, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lv, Yanhua & Ding, Ying & Song, Min & Duan, Zhiguang, 2018. "Topology-driven trend analysis for drug discovery," Journal of Informetrics, Elsevier, vol. 12(3), pages 893-905.
    2. Wang, Zhenhua & Ren, Ming & Gao, Dong & Li, Zhuang, 2023. "A Zipf's law-based text generation approach for addressing imbalance in entity extraction," Journal of Informetrics, Elsevier, vol. 17(4).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nuzhat Haneef, 2013. "Empirical research consolidation: a generic overview and a classification scheme for methods," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(1), pages 383-410, January.
    2. Guocai Chen & Michael J Cairelli & Halil Kilicoglu & Dongwook Shin & Thomas C Rindflesch, 2014. "Augmenting Microarray Data with Literature-Based Knowledge to Enhance Gene Regulatory Network Inference," PLOS Computational Biology, Public Library of Science, vol. 10(6), pages 1-16, June.
    3. Anwar, Muhammad Azfar & Zhang, Qingyu & Asmi, Fahad & Hussain, Nazim & Plantinga, Auke & Zafar, Muhammad Wasif & Sinha, Avik, 2021. "Global Perspectives on Environmental Kuznets Curve: A Bibliometric Review," MPRA Paper 110944, University Library of Munich, Germany, revised 2021.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:infome:v:9:y:2015:i:4:p:686-703. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.