Paper Info
Title
Learning open-domain comparable entity graphs from user search queries
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.
Year
DOI
Venue
2013
10.1145/2505515.2505666
CIKM
Keywords
Field
DocType
seed comparable entity pair,web search,open-domain comparable entity graph,comparable entity graph,comparable entity,various comparable entity,user search query,entity graph,commercial search engine,entity recommendation,entity pair
Graph,Data mining,Search engine,Information retrieval,Viewpoints,Bootstrapping,Computer science,Log mining,Classifier (linguistics),Empirical research,The Internet
Conference
Citations 
PageRank 
References 
1
0.35
16
Authors
6
Name
Order
Citations
PageRank
Ziheng Jiang1677.19
Lei Ji2594.49
Jianwen Zhang331914.74
Jun Yan4179885.25
Ping Guo560185.05
Ning Liu625315.62