KR2024Proceedings of the 21st International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning

Hanoi, Vietnam. November 2-8, 2024.

Edited by

ISSN: 2334-1033
ISBN: 978-1-956792-05-8

Sponsored by
Published by

Copyright © 2024 International Joint Conferences on Artificial Intelligence Organization

ASPEN: ASP-Based System for Collective Entity Resolution

  1. Zhiliang Xiang(Cardiff University, UK)
  2. Meghyn Bienvenu(Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, France, Japanese-French Laboratory for Informatics, CNRS, NII, Japan)
  3. Gianluca Cima(Sapienza University of Rome, Italy)
  4. Víctor Gutiérrez Basulto(Cardiff University, UK)
  5. Yazmín Ibáñez García(Cardiff University, UK)

Keywords

  1. Reasoning system implementations-General
  2. Empirical evaluations-General
  3. Inconsistency-tolerant / exception-tolerant reasoning-General
  4. Logic programming, answer set programming-General

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.