Advances in Logic-Based Entity Resolution: Enhancing ASPEN with Local Merges and Optimality Criteria

TL;DR

This paper introduces ASPEN+, an enhanced entity resolution system that supports local merges and new optimality criteria, improving accuracy and efficiency in resolving complex real-world data ambiguities.

Contribution

ASPEN+ extends ASPEN by formalizing local merges and optimality criteria, with comprehensive analysis and experiments demonstrating their benefits.

Findings

Local merges improve resolution accuracy in ambiguous cases.

New optimality criteria enhance solution quality and consistency.

Experimental results show improved runtime and accuracy on real datasets.

Abstract

In this paper, we present ASPEN+, which extends an existing ASP-based system, ASPEN,for collective entity resolution with two important functionalities: support for local merges and new optimality criteria for preferred solutions. Indeed, ASPEN only supports so-called global merges of entity-referring constants (e.g. author ids), in which all occurrences of matched constants are treated as equivalent and merged accordingly. However, it has been argued that when resolving data values, local merges are often more appropriate, as e.g. some instances of 'J. Lee' may refer to 'Joy Lee', while others should be matched with 'Jake Lee'. In addition to allowing such local merges, ASPEN+ offers new optimality criteria for selecting solutions, such as minimizing rule violations or maximising the number of rules supporting a merge. Our main contributions are thus (1) the formalisation and computational analysis of various notions of optimal solution, and (2) an extensive experimental evaluation on real-world datasets, demonstrating the effect of local merges and the new optimality criteria on both accuracy and runtime.