Using generalization of syntactic parse trees for taxonomy capture on the web
ICCS'11 Proceedings of the 19th international conference on Conceptual structures for discovering knowledge
Machine learning of syntactic parse trees for search and classification of text
Engineering Applications of Artificial Intelligence
Finding news story chains based on multi-dimensional event profiles
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
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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.