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dc.contributor.authorLeroy, Gondy
dc.contributor.authorChen, Hsinchun
dc.date.accessioned2004-08-16T00:00:01Z
dc.date.available2010-06-18T23:21:52Z
dc.date.issued2002en_US
dc.date.submitted2004-08-16en_US
dc.identifier.citationMedTextus: An Ontology-enhanced Medical Portal 2002,en_US
dc.identifier.urihttp://hdl.handle.net/10150/105231
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractIn this paper we describe MedTextus, an online medical search portal with dynamic search and browse tools. To search for information, MedTextus lets users request synonyms and related terms specifically tailored to their query. A mapping algorithm dynamically builds the query context based on the UMLS ontology and then selects thesaurus terms that fit this context. Users can add these terms to their query and meta-search five medical databases. To facilitate browsing, the search results can be reviewed as a list of documents per database, as a set of folders into which all the documents are automatically categorized based on their content, and as a map that is built on the fly. We designed a user study to compare these dynamic support tools with the static query support of NLM Gateway and report on initial results for the search task. The users used NLM Gateway more effectively, but used MedTextus more efficiently and preferred its query formation tools.
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.subjectMedical Librariesen_US
dc.subjectDigital Librariesen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial Intelligence laben_US
dc.subject.otherMedTextusen_US
dc.titleMedTextus: An Ontology-enhanced Medical Portalen_US
dc.typeConference Paperen_US
refterms.dateFOA2018-08-21T10:48:20Z
html.description.abstractIn this paper we describe MedTextus, an online medical search portal with dynamic search and browse tools. To search for information, MedTextus lets users request synonyms and related terms specifically tailored to their query. A mapping algorithm dynamically builds the query context based on the UMLS ontology and then selects thesaurus terms that fit this context. Users can add these terms to their query and meta-search five medical databases. To facilitate browsing, the search results can be reviewed as a list of documents per database, as a set of folders into which all the documents are automatically categorized based on their content, and as a map that is built on the fly. We designed a user study to compare these dynamic support tools with the static query support of NLM Gateway and report on initial results for the search task. The users used NLM Gateway more effectively, but used MedTextus more efficiently and preferred its query formation tools.


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