Ontology Search and Text Mining of MEDLINE Database

Hyunki Kim () and Su-Shing Chen ()
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Hyunki Kim: University of Florida
Su-Shing Chen: University of Florida

A chapter in Data Mining in Biomedicine, 2007, pp 177-189 from Springer

Abstract: Abstract With the explosion of biomedical data, information overload and users’ inability of expressing their information needs may become more serious. To solve those problems, this chapter presents a text data mining method that uses both text categorization and text clustering for building concept hierarchies for MEDLINE citations. The approach we propose is a three-step data mining process for organizing MEDLINE database: (1) categorizations according to MeSH terms, MeSH major topics, and the co-occurrence of MeSH descriptors; (2) clustering using the results of MeSH term categorization; and (3) visualization of categories and hierarchical clusters. The hierarchies automatically generated may be used to construct multiple viewpoints of a collection. Providing multiple viewpoints of a document collection and allowing users to move among these viewpoints will enable both inexperienced and experienced searchers to more fully exploit the information contained in a document collection. User interfaces with multiple viewpoints for this underlying system are also presented.

Keywords: Data mining; MEDLINE; Document Clustering; Self-Organizing Map; Multiple Viewpoints (search for similar items in EconPapers)
Date: 2007
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-0-387-69319-4_11

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DOI: 10.1007/978-0-387-69319-4_11

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