Semantic Relationship Discovery with Wikipedia Structure
Fan Bu, Yu Hao and Xiaoyan Zhu
Thanks to the idea of social collaboration, Wikipedia has accumulated vase amount of semi-structured knowledge in which the link structure faithfully reflects human's cognition on semantic relationship. Many previous graph-based methods on computing semantic relatedness with Wikipedia are originated from information retrieval, which do not distinguish two different kinds of link nodes: concepts and categories and hence cannot seize the semantic characteristics of Wikipedia stucture. In this paper, we treat concepts and categories differently and propose a novel method RCRank to jointly compute concept-concept relatedness and concept-category relatedness base on the assumption that information carried in concept-concept links and concept-category links can mutually reinforce each other. Different from previous work, RCRank is able to discover the semantic relationship between concepts. Specifically, it can not only find semantically related concepts but also interpret their relations by categories. Experimental results on concept recommendation and relation interpretation show that our method substantially outperforms classical methods.