CoRE: A context-aware relation extraction method for relation completion

Zhixu Li, Mohamed A. Sharaf, Laurianne Sitbon, Xiaoyong Du, Xiaofang Zhou

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

We identify relation completion (RC) as one recurring problem that is central to the success of novel big data applications such as Entity Reconstruction and Data Enrichment. Given a semantic relation {\cal R}, RC attempts at linking entity pairs between two entity lists under the relation {\cal R}. To accomplish the RC goals, we propose to formulate search queries for each query entity \alpha based on some auxiliary information, so that to detect its target entity \beta from the set of retrieved documents. For instance, a pattern-based method (PaRE) uses extracted patterns as the auxiliary information in formulating search queries. However, high-quality patterns may decrease the probability of finding suitable target entities. As an alternative, we propose CoRE method that uses context terms learned surrounding the expression of a relation as the auxiliary information in formulating queries. The experimental results based on several real-world web data collections demonstrate that CoRE reaches a much higher accuracy than PaRE for the purpose of RC.

Original languageEnglish
Article number6587241
Pages (from-to)836-849
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume26
Issue number4
DOIs
Publication statusPublished - Apr 2014
Externally publishedYes

Keywords

  • Context-aware relation extraction
  • Relation completion
  • Relation query expansion

ASJC Scopus subject areas

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

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