Entity matching across heterogeneous data sources: An approach based on constrained cascade generalization

Huimin Zhao, Sudha Ram

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

To integrate or link the data stored in heterogeneous data sources, a critical problem is entity matching, i.e., matching records representing semantically corresponding entities in the real world, across the sources. While decision tree techniques have been used to learn entity matching rules, most decision tree learners have an inherent representational bias, that is, they generate univariate trees and restrict the decision boundaries to be axis-orthogonal hyper-planes in the feature space. Cascading other classification methods with decision tree learners can alleviate this bias and potentially increase classification accuracy. In this paper, the authors apply a recently-developed constrained cascade generalization method in entity matching and report on empirical evaluation using real-world data. The evaluation results show that this method outperforms the base classification methods in terms of classification accuracy, especially in the dirtiest case.

Original languageEnglish (US)
Pages (from-to)368-381
Number of pages14
JournalData and Knowledge Engineering
Volume66
Issue number3
DOIs
StatePublished - Sep 1 2008

Keywords

  • Cascade generalization
  • Decision tree
  • Entity matching
  • Heterogeneous databases
  • Record linkage

ASJC Scopus subject areas

  • Information Systems and Management

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