TY - GEN
T1 - Measuring semantic similarity between named entities by searching the Web directory
AU - Jiahui, Liu
AU - Birnbaum, Lawrence A
PY - 2007
Y1 - 2007
N2 - The importance of named entities in information retrieval and knowledge management has recently brought interest in characterizing semantic relationships between entities. In this paper, we propose a method for measuring semantic similarity, an important type of semantic relationship, between entities. The method is based on Google Directory, a search interface to the Open Directory Project. Via the search engine, we can locate the web pages relevant to an entity and automatically create a profile of the entity according to the directory assignments of its web pages, which capture various features of the entity. Using their profiles, the semantic similarity between entities can be measured in different dimensions. We apply the semantic similarity measurement to two knowledge acquisition tasks: thesaurus construction of entities and fine grained categorization of entities. Our experiments demonstrate that the proposed method works effectively in these two tasks.
AB - The importance of named entities in information retrieval and knowledge management has recently brought interest in characterizing semantic relationships between entities. In this paper, we propose a method for measuring semantic similarity, an important type of semantic relationship, between entities. The method is based on Google Directory, a search interface to the Open Directory Project. Via the search engine, we can locate the web pages relevant to an entity and automatically create a profile of the entity according to the directory assignments of its web pages, which capture various features of the entity. Using their profiles, the semantic similarity between entities can be measured in different dimensions. We apply the semantic similarity measurement to two knowledge acquisition tasks: thesaurus construction of entities and fine grained categorization of entities. Our experiments demonstrate that the proposed method works effectively in these two tasks.
UR - http://www.scopus.com/inward/record.url?scp=48349136698&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=48349136698&partnerID=8YFLogxK
U2 - 10.1109/WI.2007.75
DO - 10.1109/WI.2007.75
M3 - Conference contribution
AN - SCOPUS:48349136698
SN - 0769530265
SN - 9780769530260
T3 - Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007
SP - 461
EP - 465
BT - Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007
T2 - IEEE/WIC/ACM International Conference on Web Intelligence, WI 2007
Y2 - 2 November 2007 through 5 November 2007
ER -