Deep web entity identification method with unique constraint

Xuefeng Xian, Pengpeng Zhao, Zhaobin Liu, Caidong Gu, Victor S. Sheng

Research output: Contribution to journalArticlepeer-review

Abstract

In practice, some attributes meet a unique constraint: each entity has a unique value for the attribute. A deep web entity identification method was presented to solve problems of data error correction, uniqueness constraint enforcement, and local data fusion in deep web data integration. The method transformed the entity identification phrase to a k-partite graph clustering problem, considering both similarity and association of attribute values. Moreover, it performed global record linkage and data fusion simultaneously and could identify incorrect values and differentiate them from correct ones at the beginning. Experimental results demonstrate the high precision and scalability of our method.

Original languageEnglish
Pages (from-to)2470-2482
Number of pages13
JournalInternational Journal of Performability Engineering
Volume14
Issue number10
DOIs
StatePublished - Oct 2018

Keywords

  • Clustering
  • Data fusion
  • Entity identification
  • K-partite graph
  • Match
  • Record linkage

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