ASPEN: ASP-Based System for Collective Entity Resolution

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.

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Published
2024-10-26
DOI
https://doi.org/10.24963/kr.2024/74
Primary Topic
Topic Modeling
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article
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article

ASPEN: ASP-Based System for Collective Entity Resolution

Zhiliang Xiang, Víctor Gutiérrez Basulto, Gianluca Cima, Meghyn Bienvenu et al.
Topic Modeling
article

ASPEN: ASP-Based System for Collective Entity Resolution

Zhiliang Xiang, Víctor Gutiérrez Basulto, Gianluca Cima, Meghyn Bienvenu, Yazmín Ibáñez García
article en

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.

Centre National de la Recherche Scientifique (FR), Université de Bordeaux (FR), International Research Center for Japanese Studies (JP), Laboratoire Bordelais de Recherche en Informatique (FR), Institut Polytechnique de Bordeaux (FR), Japanese-French Laboratory for Informatics (JP), Cardiff University (GB), Sapienza University of Rome (IT)
Agence Nationale de la Recherche
Openalex Percentile: Top 83%
Topic Modeling
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