Journal
DATA & KNOWLEDGE ENGINEERING
Volume 66, Issue 3, Pages 368-381Publisher
ELSEVIER
DOI: 10.1016/j.datak.2008.04.007
Keywords
heterogeneous databases; entity matching; record linkage; decision tree; cascade generalization
Ask authors/readers for more resources
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. (c) 2008 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available