Customizable and Ontology-Enhanced Medical Information Retrieval Interfaces
| dc.contributor.author | Leroy, Gondy | |
| dc.contributor.author | Tolle, Kristin M. | |
| dc.contributor.author | Chen, Hsinchun | |
| dc.date.accessioned | 2004-08-16T00:00:01Z | |
| dc.date.available | 2010-06-18T23:20:14Z | |
| dc.date.issued | 1999 | en_US |
| dc.date.submitted | 2004-08-16 | en_US |
| dc.identifier.citation | Customizable and Ontology-Enhanced Medical Information Retrieval Interfaces 1999, | en_US |
| dc.identifier.uri | http://hdl.handle.net/10150/105149 | |
| dc.description | Artificial Intelligence Lab, Department of MIS, University of Arizona | en_US |
| dc.description.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. | |
| dc.format.mimetype | application/pdf | en_US |
| dc.language.iso | en | en_US |
| dc.subject | National Science Digital Library | en_US |
| dc.subject | NSDL | en_US |
| dc.subject | Artificial Intelligence lab | en_US |
| dc.subject | AI lab | en_US |
| dc.subject | Medical information retrieval | en_US |
| dc.subject | Ontologies | en_US |
| dc.subject | UMLS | en_US |
| dc.subject | Deep semantic parsing | en_US |
| dc.subject | Human Computer Interaction | en_US |
| dc.subject | Medical Libraries | en_US |
| dc.subject | Information Seeking Behaviors | en_US |
| dc.title | Customizable and Ontology-Enhanced Medical Information Retrieval Interfaces | en_US |
| dc.type | Conference Paper | en_US |
| refterms.dateFOA | 2018-06-18T01:25:23Z | |
| html.description.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. |
