The Effect of Transitive Closure on the Calibration of Logistic Regression for Entity Resolution

Document Type: Research Paper

Authors

1 MSC, Department of Information Quality Program, University of Arkansas at Little Rock, Arkansas, USA.

2 Prof., Department of Information Science, University of Arkansas at Little Rock, Arkansas, USA.

Abstract

This paper describes a series of experiments in using logistic regression machine learning as a method for entity resolution. From these experiments the authors concluded that when a supervised ML algorithm is trained to classify a pair of entity references as linked or not linked pair, the evaluation of the model’s performance should take into account the transitive closure of its pairwise linking decisions, not just the pairwise classifications alone. Part of the problem is that the measures of precision and recall as calculated in data mining classification algorithms such as logistic regression is different from applying these measures to entity resolution (ER) results.. As a classifier, logistic regression precision and recall measure the algorithm’s pairwise decision performance. When applied to ER, precision and recall measure how accurately the set of input references were partitioned into subsets (clusters) referencing the same entity. When applied to datasets containing more than two references, ER is a two-step process. Step One is to classify pairs of records as linked or not linked. Step Two applies transitive closure to these linked pairs to find the maximally connected subsets (clusters) of equivalent references. The precision and recall of the final ER result will generally be different from the precision and recall measures of the pairwise classifier used to power the ER process. The experiments described in the paper were performed using a well-tested set of synthetic customer data for which the correct linking is known. The best F-measure of precision and recall for the final ER result was obtained by substantially increasing the threshold of the logistic regression pairwise classifier.

Keywords

Main Subjects


Christen, P. (2014). Data Matching Concepts and Techniques for Record Linkage. Entity Resolution, and Duplicate Detection. Berlin: Springer Berlin.
Eram, A., Mohammed, A.G., Pillai, V. & Talburt, J.R. (2017). Comparing the Effectiveness of Deterministic Matching with Probabilistic Matching for Entity Resolution of Student Enrollment Records. MIT International Conference on Information Quality, Little Rock, AR, Oct 6-7.
Fellegi, I. P., & Sunter, A. B. (1969). A Theory for Record Linkage. Journal of the American Statistical Association, 64(328), 1183-1210.
Kobayashi, F., Eram, A., & Talburt, J. (2018). Comparing the Performance of Logistic Regression Classification to Rule-Based Entity Resolution. 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). DOI: 10.1109/mipr.2018.00033.
Talburt, J. R., Zhou, Y., & Shivaiah, S. Y. (2009). SOG: A synthetic occupancy generator to support entity resolution instruction and research. 2009 International Conference on Information Quality, Potsdam, Germany, pp. 91-105.
Talburt, J.R. & Zhou, Y. (2015). Entity information life cycle for Big Data: Master data management and information integration. Elsevier. Waltham, MA.
Zhang, J., Bheemavaram, R., & Li, W. N. (2009). Transitive Closure of Data Records: Application and Computation. Data Engineering International Series in Operations Research & Management Science. DOI: 10.1007/978-1-4419-0176-7_3.
Zhou, Y., & Talburt, J. R. (2011). Entity identity information management (EIIM). MIT International Conference on Information Quality, 237-341.