title: Multiple categorization of search results.
abstract: The number of publications available to physicians and patients is increasing at an alarming rate. Although users can use tools to assist in reformulating their query, this approach is ineffective when their information needs are imprecise or many documents are relevant. The ranked list presentation of documents provides little or no information relating documents to the initial query or to each other. We argue that information overload can be reduced if documents are placed in categories that (a) relate to the initial query and (b) contain a manageable number of documents. Dynamic Categorization is a knowledge-based approach that satisfies (a), however the number of documents in a category may still be large. We demonstrate that using the same approach to re-categorize the documents in large categories reduces the number of documents in the subcategory, and maintains a clear relationship to the initial query.
authors.affiliation: Information and Computer Science Department, University of California, Irvine, USA. cblake@ics.uci.edu
authors.forename: C
authors.initials: C
authors.lastname: Blake
authors.forename: W
authors.initials: W
authors.lastname: Pratt
mesh_terms: D001943:Breast Neoplasms / Q000517:prevention & control; D019992:Databases as Topic; D005260:Female; D006331:Heart Diseases / Q000175:diagnosis; D006801:Humans; D016247:Information Storage and Retrieval / Q000145:classification* / Q000379:methods; D011379:Prognosis