SHINE+: A General Framework for Domain-Specific Entity Linking with Heterogeneous Information Networks

Wei Shen, Jiawei Han, Jianyong Wang, Xiaojie Yuan, Zhenglu Yang

Research output: Contribution to journalArticle

Abstract

Heterogeneous information networks that consist of multi-type, interconnected objects are becoming increasingly popular, such as social media networks and bibliographic networks. The task of linking named entity mentions detected from unstructured Web text with their corresponding entities in a heterogeneous information network is of practical importance for the problem of information network population. This task is challenging due to name ambiguity and limited knowledge existing in the network. Most existing entity linking methods focus on linking entities with Wikipedia and cannot be applied to our task. In this paper, we present SHINE+, a general framework for linking named entitieS in Web free text with a Heterogeneous I nformation NEtwork. We propose a probabilistic linking model, which unifies an entity popularity model with an entity object model. As the entity knowledge contained in the information network is insufficient, we propose a knowledge population algorithm to iteratively enrich the network entity knowledge by leveraging the context information of mentions mapped by the linking model with high confidence, which subsequently boosts the linking performance. Experimental results over two real heterogeneous information networks (i.e., DBLP and IMDb) demonstrate the effectiveness and efficiency of our proposed framework in comparison with the baselines.

Original languageEnglish (US)
Article number7990163
Pages (from-to)353-366
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume30
Issue number2
DOIs
StatePublished - Feb 1 2018

Keywords

  • Entity linking
  • heterogeneous information network
  • knowledge population algorithm
  • probabilistic linking model

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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