Combinatorial topology-based semantic clustering applied to pubmed

Wen Wen Yang, I. Jen Chiang, Ruey Ling Yeh, Hsiang Chun Tsai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

To confront with an ever increasing number of published scientific articles, an effective, efficient, and easy-to-use tool is required to support biomedical scientists, while entering a new scientific field and encountering clinical decision, to organize a vast amount of PubMed abstracts into the panorama of specific topics according to their relevance. In brief, the set of associations among frequently co-occurring terms in given a set of PubMed documents forms naturally a simplicial complex. Afterwards each connected component of this simplicial complex represents a concept in the collection. Based on these concepts, documents can be clustered into meaningful classes. This paper presents an alternative search engine that applies a combinatorial topological method to automatically extract semantic clusters from the PubMed database of biomedical literature. We use several qualitative parameters to perform the user study that shows users are able to reduce search time. This clustering search engine is publicly available at http://ginni.bme.ntu.edu.tw/.

Original languageEnglish
Title of host publicationSecond International Conference on Innovative Computing, Information and Control, ICICIC 2007
DOIs
Publication statusPublished - 2008
Event2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007 - Kumamoto, Japan
Duration: Sep 5 2007Sep 7 2007

Other

Other2nd International Conference on Innovative Computing, Information and Control, ICICIC 2007
CountryJapan
CityKumamoto
Period9/5/079/7/07

ASJC Scopus subject areas

  • Computer Science(all)
  • Mechanical Engineering

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