Learning to Rank Entities from Dual Feature Spaces
ZHAO Yixin1, NIU Shuzi1, JI Chunyan2, LU Fei2, XU Rui3
1.Institute of Software, Chinese Academy of Sciences, Beijing 100190, China; 2.Qilu Hospital of Shandong University, Jinan, Shandong 250012, China; 3.Sinoparasoft Company Limited, Beijing 100190, China
Abstract：Entity retrieval from knowledge graph is of substantial significance as the large scale knowledge graphs appear, and the industry demand on effectively managing the domain knowledge graphs. Given a certain knowledge graph and a user query, entity retrieval aims at obtaining a ranking list of entities from the knowledge graph accor-ding to its relevance to the query. Being treated as the matching between the query and entities, traditional entity retrieval models map both user queries and entities into the word feature space. However, it does not work when two words in the name of an entity are assumed to be independent. In this paper, we propose to project both user queries and entities into a dual feature space, namely entity-word feature space. First, we represent entities as multiple domains and extract ranking features from them. Then, learning to rank models are employed to train a ranking model from this dual feature space. Experimental results on benchmark datasets show that our proposed method outperform state-of-the-art baselines significantly.
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2020, Vol. 34 
