NDLI: Learning open-domain comparable entity graphs from user search queries

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Learning open-domain comparable entity graphs from user search queries

Content Provider	ACM Digital Library
Author	Ji, Lei Zhang, Jianwen Jiang, Ziheng Yan, Jun Guo, Ping Liu, Ning
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.
Starting Page	2339
Ending Page	2344
Page Count	6
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505666
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Comparable entity mining Open-domain Log mining
Content Type	Text
Resource Type	Article

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