Machine Learning Comparison in Entity Resolution

Abstract

This paper describes experiments using and comparing multiple machine learning (ML) algorithms for entity resolution (ER). In these experiments, person references were classified as "linked" or "not linked" by the four different methods. The objective of the experiments was to compare the linking performance of each method to evaluate the effectiveness of various ML techniques as an extension or augmentation to existing ER Systems. Each experiment used synthetic data. In this paper, some promising empirical results are reported that demonstrate favorable performance of the ML techniques.