Customizable and Ontology-Enhanced Medical Information Retrieval Interfaces

Persistent Link:
http://hdl.handle.net/10150/105149
Title:
Customizable and Ontology-Enhanced Medical Information Retrieval Interfaces
Author:
Leroy, Gondy; Tolle, Kristin M.; Chen, Hsinchun
Citation:
Customizable and Ontology-Enhanced Medical Information Retrieval Interfaces 1999,
Issue Date:
1999
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/105149
Submitted date:
2004-08-16
Abstract:
This paper describes the development and testing of the Medical Concept Mapper as an aid to providing synonyms and semantically related concepts to improve searching. All terms are related to the userquery and fit into the query context. The system is unique because its five components combine humancreated and computer-generated elements. The Arizona Noun Phraser extracts phrases from natural language user queries. WordNet and the UMLS Metathesaurus provide synonyms. The Arizona Concept Space generates conceptually related terms. Semantic relationships between queries and concepts are established using the UMLS Semantic Net. Two user studies conducted to evaluate the system are described.
Type:
Conference Paper
Language:
en
Keywords:
Human Computer Interaction; Medical Libraries; Information Seeking Behaviors
Local subject classification:
National Science Digital Library; NSDL; Artificial Intelligence lab; AI lab; Medical information retrieval; Ontologies; UMLS; Deep semantic parsing

Full metadata record

DC FieldValue Language
dc.contributor.authorLeroy, Gondyen_US
dc.contributor.authorTolle, Kristin M.en_US
dc.contributor.authorChen, Hsinchunen_US
dc.date.accessioned2004-08-16T00:00:01Z-
dc.date.available2010-06-18T23:20:14Z-
dc.date.issued1999en_US
dc.date.submitted2004-08-16en_US
dc.identifier.citationCustomizable and Ontology-Enhanced Medical Information Retrieval Interfaces 1999,en_US
dc.identifier.urihttp://hdl.handle.net/10150/105149-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractThis paper describes the development and testing of the Medical Concept Mapper as an aid to providing synonyms and semantically related concepts to improve searching. All terms are related to the userquery and fit into the query context. The system is unique because its five components combine humancreated and computer-generated elements. The Arizona Noun Phraser extracts phrases from natural language user queries. WordNet and the UMLS Metathesaurus provide synonyms. The Arizona Concept Space generates conceptually related terms. Semantic relationships between queries and concepts are established using the UMLS Semantic Net. Two user studies conducted to evaluate the system are described.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.subjectHuman Computer Interactionen_US
dc.subjectMedical Librariesen_US
dc.subjectInformation Seeking Behaviorsen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial Intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherMedical information retrievalen_US
dc.subject.otherOntologiesen_US
dc.subject.otherUMLSen_US
dc.subject.otherDeep semantic parsingen_US
dc.titleCustomizable and Ontology-Enhanced Medical Information Retrieval Interfacesen_US
dc.typeConference Paperen_US
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