Learning open-domain comparable entity graphs from user search queries

Authors:
Ziheng Jiang;Lei Ji;Jianwen Zhang;Jun Yan;Ping Guo;Ning Liu
Affiliations:
Beijing Institute of Technology, Beijing, China;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China;Beijing Institute of Technology, Beijing, China;Microsoft Research Asia, Beijing, China
Venue:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Year:
2013

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Abstract

A frequent behavior of internet users is to compare among various comparable entities for decision making. As an instance, a user may compare among iPhone 5, Lumia 920 etc. products before deciding which cellphone to buy. However, it is a challenging problem to know what entities are generally comparable from the users' viewpoints in the open domain Web. In this paper, we propose a novel solution, which is known as Comparable Entity Graph Mining (CEGM), to learn an open-domain comparable entity graph from the user search queries. CEGM firstly mine seed comparable entity pairs from user search queries automatically using predefined query patterns. Next, it discovers more entity pairs with a confidence classifier in a bootstrapping fashion. Newly discovered entity pairs are organized into an open-domain comparable entity graph. Based on our empirical study over 1 billion queries of a commercial search engine, we build a comparable entity graph which covers 73.4% queries in the top 50 million unique queries of a commercial search engine. Through manual labeling in sampled sub-graphs, the average precision of comparable entities is 89.4%. As applications of the learned entity graph, the entity recommendation in Web search is empirically studied.