TY - GEN
T1 - Ontology clarification by using semantic disambiguation
AU - Guo, Lei
AU - Wang, Xiaodong
AU - Fang, Jun
PY - 2008
Y1 - 2008
N2 - Semantic Web technology highly depends on the quality of ontology. In order to enhance quality of ontology, a vast amount of research has focused on concept modeling task, but there is one major problem with lexical representation of ontology. Current lexical representation is term which may have different meanings, so as to result in frustrating misunderstanding and ambiguity during the 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. Ontology clarification is the process of disambiguating terms in ontology by using its surrounding ontology elements 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 for semantic relatedness between them. Experiments show our ontology clarification method is valid. 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. Another experiment proves that our method is also effective in a semi-automatic process.
AB - Semantic Web technology highly depends on the quality of ontology. In order to enhance quality of ontology, a vast amount of research has focused on concept modeling task, but there is one major problem with lexical representation of ontology. Current lexical representation is term which may have different meanings, so as to result in frustrating misunderstanding and ambiguity during the 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. Ontology clarification is the process of disambiguating terms in ontology by using its surrounding ontology elements 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 for semantic relatedness between them. Experiments show our ontology clarification method is valid. 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. Another experiment proves that our method is also effective in a semi-automatic process.
KW - Ontology clarification
KW - Semantic relatedness
KW - Word sense disambiguation
UR - http://www.scopus.com/inward/record.url?scp=51049086162&partnerID=8YFLogxK
U2 - 10.1109/CSCWD.2008.4537025
DO - 10.1109/CSCWD.2008.4537025
M3 - 会议稿件
AN - SCOPUS:51049086162
SN - 9781424416509
T3 - Proceedings of the 2008 12th International Conference on Computer Supported Cooperative Work in Design, CSCWD
SP - 476
EP - 481
BT - Proceedings of the 2008 12th International Conference on Computer Supported Cooperative Work in Design, CSCWD
T2 - 2008 12th International Conference on Computer Supported Cooperative Work in Design, CSCWD
Y2 - 16 April 2008 through 18 April 2008
ER -