Terminology-based knowledge mining for new knowledge discovery

Hideki Mima, Sophia Ananiadou, Katsumori Matsushima

    Research output: Contribution to journalArticlepeer-review


    In this article we present an integrated knowledge-mining system for the domain of biomedicine, in which automatic term recognition, term clustering, information retrieval, and visualization are combined. The primary objective of this system is to facilitate knowledge acquisition from documents and aid knowledge discovery through terminology-based similarity calculation and visualization of automatically structured knowledge. This system also supports the integration of different types of databases and simultaneous retrieval of different types of knowledge. In order to accelerate knowledge discovery, we also propose a visualization method for generating similarity-based knowledge maps. The method is based on real-time terminology-based knowledge clustering and categorization and allows users to observe real-time generated knowledge maps, graphically. Lastly, we discuss experiments using the GENIA corpus to assess the practicality and applicability of the system. © 2006 ACM.
    Original languageEnglish
    Pages (from-to)74-88
    Number of pages14
    JournalACM Transactions on Asian Language Information Processing
    Issue number1
    Publication statusPublished - 2006


    • Automatic term recognition
    • Biomedicine
    • Natural language processing
    • Structuring knowledge
    • Terminology
    • Visualization


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