Title
PatternRank+NN: A Ranking Framework Bringing User Behaviors into Entity Set Expansion from Web Search Queries
Abstract
We propose a ranking framework, called PatternRank+NN, for expanding a set of seed entities of a particular class (i.e., entity set expansion) from Web search queries. PatternRank+NN consists of two parts: PatternRank and NN. Unlike the traditional methods, PatternRank brings user behaviors into entity set expansion from Web search queries. PatternRank is a Markov chain which simulates the Web search query process of users on the graph model for Web search query log, and ranks the features of the class. The features in the front rank are used to generate candidate entities of the class. NN, a ranking strategy called Nearest Neighbor, ranks these candidate entities such that the set of seed entities can be expanded from the candidate entities in the front rank. Our experiments demonstrate the superior performance of PatternRank+NN in comparison with the state-of-the-art methods.
Year
DOI
Venue
2020
10.1145/3386042
ACM Transactions on the Web
Keywords
DocType
Volume
Entity set expansion,Markov chain,Nearest Neighbor,User behavior
Journal
14
Issue
ISSN
Citations 
3
1559-1131
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Zhijun Xiao100.34
Cuiping Li2399.19
Hong Chen335938.55