TY - GEN
T1 - Handling polysemy in description logic ontologies
AU - Fang, Jun
AU - Guo, Lei
AU - Yang, Ning
PY - 2010
Y1 - 2010
N2 - Semantic Web technology highly depends on the quality of ontology as it reduces or eliminates conceptual confusion and reuses knowledge. In order to enhance quality of ontology, there is one major problem with lexical representation of ontology. Current lexical representation is term which may have different meanings, i.e., the term is polysemous, this can result in frustrating misunderstanding and ambiguity during the management and application of ontology. To solve this problem, sense is used to replace term as the lexical representation of concepts and properties for its unique meaning. The process of handling polysemy is to automatically disambiguate terms in ontology by using its surrounding ontology symbols and its nearby terms in annotated documents using this ontology. The right sense is assigned to a target term by maximizing the relatedness between the target and its neighbors. Experiments show our method has good performance. Comparing with the best word sense disambiguation method, the concept precision is almost 2 times than the precision of noun, and the property precision is almost 3 times than the precision of verb. The last experiment proves that our method is also effective in a semi-automatic process.
AB - Semantic Web technology highly depends on the quality of ontology as it reduces or eliminates conceptual confusion and reuses knowledge. In order to enhance quality of ontology, there is one major problem with lexical representation of ontology. Current lexical representation is term which may have different meanings, i.e., the term is polysemous, this can result in frustrating misunderstanding and ambiguity during the management and application of ontology. To solve this problem, sense is used to replace term as the lexical representation of concepts and properties for its unique meaning. The process of handling polysemy is to automatically disambiguate terms in ontology by using its surrounding ontology symbols and its nearby terms in annotated documents using this ontology. The right sense is assigned to a target term by maximizing the relatedness between the target and its neighbors. Experiments show our method has good performance. Comparing with the best word sense disambiguation method, the concept precision is almost 2 times than the precision of noun, and the property precision is almost 3 times than the precision of verb. The last experiment proves that our method is also effective in a semi-automatic process.
UR - http://www.scopus.com/inward/record.url?scp=78649304237&partnerID=8YFLogxK
U2 - 10.1109/FSKD.2010.5569417
DO - 10.1109/FSKD.2010.5569417
M3 - 会议稿件
AN - SCOPUS:78649304237
SN - 9781424459346
T3 - Proceedings - 2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010
SP - 1793
EP - 1797
BT - Proceedings - 2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010
T2 - 2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010
Y2 - 10 August 2010 through 12 August 2010
ER -