Performance Bounds for Pairwise Entity Resolution

TL;DR

This paper establishes theoretical bounds linking small-scale validation performance to large-scale dataset performance in pairwise entity resolution, facilitating scalable optimization using limited labeled data.

Contribution

It introduces bounding properties that connect small validation set performance to large dataset performance, aiding scalable entity resolution.

Findings

Proves bounds between small and large dataset performance

Enables optimization of algorithms with limited labeled data

Supports scaling entity resolution to massive datasets

Abstract

One significant challenge to scaling entity resolution algorithms to massive datasets is understanding how performance changes after moving beyond the realm of small, manually labeled reference datasets. Unlike traditional machine learning tasks, when an entity resolution algorithm performs well on small hold-out datasets, there is no guarantee this performance holds on larger hold-out datasets. We prove simple bounding properties between the performance of a match function on a small validation set and the performance of a pairwise entity resolution algorithm on arbitrarily sized datasets. Thus, our approach enables optimization of pairwise entity resolution algorithms for large datasets, using a small set of labeled data.