Entity identification for heterogeneous database integration—a multiple classifier system approach and empirical evaluation

This research develops and evaluates a novel multiple classifier system approach that improves entity identification accuracy and applies various classification techniques drawn from statistical pattern recognition, machine learning, and artificial neural networks to determine whether two records from different data sources represent the same real-world entity.

Abstract

Entity identification, i.e., detecting semantically corresponding records from heterogeneous data sources, is a critical step in integrating the data sources. The objective of this research is to develop and evaluate a novel multiple classifier system approach that improves entity identification accuracy. We apply various classification techniques drawn from statistical pattern recognition, machine learning, and artificial neural networks to determine whether two records from different data sources represent the same real-world entity. We further employ a variety of ways to combine multiple classifiers for improved classification accuracy. In this paper, we report on some promising empirical results that demonstrate performance improvement by combining multiple classifiers.