User guided entity similarity search using meta-path selection in heterogeneous information networks

Authors:
Xiao Yu;Yizhou Sun;Brandon Norick;Tiancheng Mao;Jiawei Han
Affiliations:
University of Illinois, at Urbana-Champaign, Champaign, IL, USA;University of Illinois, at Urbana-Champaign, Champaign, IL, USA;University of Illinois, at Urbana-Champaign, Champaign, IL, USA;University of Illinois, at Urbana-Champaign, Champaign, IL, USA;University of Illinois, at Urbana-Champaign, Champaign, IL, USA
Venue:
Proceedings of the 21st ACM international conference on Information and knowledge management
Year:
2012

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Abstract

With the emergence of web-based social and information applications, entity similarity search in information networks, aiming to find entities with high similarity to a given query entity, has gained wide attention. However, due to the diverse semantic meanings in heterogeneous information networks, which contain multi-typed entities and relationships, similarity measurement can be ambiguous without context. In this paper, we investigate entity similarity search and the resulting ambiguity problems in heterogeneous information networks. We propose to use a meta-path-based ranking model ensemble to represent semantic meanings for similarity queries, exploit the possibility of using using user-guidance to understand users query. Experiments on real-world datasets show that our framework significantly outperforms competitor methods.