2024/08/13 by Zhiliang Xiang, Meghyn Bienvenu, Xiang, Zhiliang +7 · 1 citation
Computer Science · Decision Sciences · #Advanced Database Systems and Queries #Data Quality and Management #Databases (cs.DB) #FOS: Computer and information sciences #Network Security and Intrusion Detection
paper · pdf · doi:10.48550/arxiv.2408.06961
openalex publication_date 2024/08/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.