A Document Clustering and Ranking System for Exploring MEDLINE Citations | Zendy

Yen-Yin Lin | Zendy; Wei Li | Zendy; Ke Chen | Zendy; Yang Liu | Zendy

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A Document Clustering and Ranking System for Exploring MEDLINE Citations

Author(s) -

Yen-Yin Lin,

Wei Li,

Ke Chen,

Yang Liu

Publication year - 2007

Publication title -

journal of the american medical informatics association

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.614

H-Index - 150

eISSN - 1527-974X

pISSN - 1067-5027

DOI - 10.1197/jamia.m2215

Subject(s) - information retrieval , computer science , automatic summarization , ranking (information retrieval) , cluster analysis , medline , citation , world wide web , artificial intelligence , political science , law

A major problem faced in biomedical informatics involves how best to present information retrieval results. When a single query retrieves many results, simply showing them as a long list often provides poor overview. With a goal of presenting users with reduced sets of relevant citations, this study developed an approach that retrieved and organized MEDLINE citations into different topical groups and prioritized important citations in each group.

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