ASPEN: ASP-Based System for Collective Entity Resolution

TL;DR

ASPEN introduces an efficient ASP-based framework for collective entity resolution, addressing practical challenges and demonstrating high accuracy and insightful performance analysis on real-world datasets.

Contribution

The paper develops new ASP encodings and variants, including Datalog approximations, to improve the efficiency and effectiveness of collective entity resolution.

Findings

High accuracy achieved on real-world datasets

Different encoding variants impact performance and accuracy

Recursion and approximation methods influence solution quality

Abstract

In this paper, we present ASPEN, an answer set programming (ASP) implementation of a recently proposed declarative framework for collective entity resolution (ER). While an ASP encoding had been previously suggested, several practical issues had been neglected, most notably, the question of how to efficiently compute the (externally defined) similarity facts that are used in rule bodies. This leads us to propose new variants of the encodings (including Datalog approximations) and show how to employ different functionalities of ASP solvers to compute (maximal) solutions, and (approximations of) the sets of possible and certain merges. A comprehensive experimental evaluation of ASPEN on real-world datasets shows that the approach is promising, achieving high accuracy in real-life ER scenarios. Our experiments also yield useful insights into the relative merits of different types of (approximate) ER solutions, the impact of recursion, and factors influencing performance.