HEEL: exploratory entity linking for heterogeneous information networks

  • Chengyu Wang
  • Xiaofeng HeEmail author
  • Aoying Zhou
Regular Paper


A heterogeneous information network (HIN) is a ubiquitous data model, consisting of multiple types of entities and relations. Names of entities in HINs are inherently ambiguous, making it difficult to fully disambiguate a HIN. In this paper, we introduce the task of exploratory entity linking for HINs. Given a partially disambiguated HIN, we aim at linking ambiguous names to disambiguated entities in the HIN if their referent entities are present. We also try to “explore” other alternatives by discovering new entities and adding them to the HIN. A partial classification EM-based approach is proposed to address this task. We present a constrained probability propagation model to link surface names to entities in the HIN. New entity detection process is modeled as a maximum edge weight clique problem. Experiments illustrate that our method outperforms state-of-the-art methods for entity linking with HINs and author name disambiguation.


Heterogeneous information network Exploratory entity linking Partial classification EM Author name disambiguation 



This work is supported by the National Key Research and Development Program of China under Grant No. 2016YFB1000904. Chengyu Wang is partially supported by the Outstanding Doctoral Dissertation Cultivation Plan of Action under Grant No. YB2016040.


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© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer Science and Software EngineeringEast China Normal UniversityShanghaiChina
  2. 2.School of Data Science and EngineeringEast China Normal UniversityShanghaiChina

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