Terminology-based knowledge mining for new knowledge discovery

Hideki Mima, Sophia Ananiadou, Katsumori Matsushima

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)74-88
Number of pages15
JournalACM Transactions on Asian Language Information Processing
Issue number1
Publication statusPublished - 2006
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science(all)


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