Entity ranking for descriptive queries

Kai Hong, Pengjun Pei, Ye-Yi Wang, Dilek Hakkani-Tür

Abstract: We investigate the problem of entity ranking towards descriptive queries, that aims to match entities referred in user queries to entities of a large knowledge base (KB). Entity ranking faces the primary challenge of the sparseness of entity related data, such as various ways of referring to an entity. The lack of sufficient variations of entity referring expressions in KB makes it difficult to find entities referred in user queries, especially when the queries are descriptive. We tackle this problem by enriching KB entries using web documents and query click logs. First, we propose a novel method of injecting textual information from web documents to the KB on a large scale. Since the number of web documents can be large, we propose to use keyword extraction and summarization techniques for compactly representing entity-related information. Second, we mine web search query logs to link entities to existing queries. Experiments show significant improvements after the KB enrichment, compared with two competitive baselines. We also achieve further improvements by combining the data from these two resources.