{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T07:00:56Z","timestamp":1762326056708,"version":"build-2065373602"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:p>In this paper, we present ASPEN+, which extends an existing\n\nASP-based system, ASPEN,for collective entity resolution\n\nwith two important functionalities: support for local\n\nmerges and new optimality criteria for preferred solutions.\n\nIndeed, ASPEN only supports so-called global merges of\n\nentity-referring constants (e.g. author ids), in which all\n\noccurrences of matched constants are treated as equivalent\n\nand merged accordingly. However, it has been argued that\n\nwhen resolving data values, local merges are often more\n\nappropriate, as e.g. some instances of \u2018J. Lee\u2019 may refer\n\nto \u2018Joy Lee\u2019, while others should be matched with \u2018Jake\n\nLee\u2019. In addition to allowing such local merges, ASPEN+\n\noffers new optimality criteria for selecting solutions,\n\nsuch as minimizing rule violations or maximising the number\n\nof rules supporting a merge. Our main contributions are\n\nthus (1) the formalisation and computational analysis of\n\nvarious notions of optimal solution, and (2) an extensive\n\nexperimental evaluation on real-world datasets,\n\ndemonstrating the effect of local merges and the new\n\noptimality criteria on both accuracy and runtime.<\/jats:p>","DOI":"10.24963\/kr.2025\/64","type":"proceedings-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:10:44Z","timestamp":1762323044000},"page":"659-669","source":"Crossref","is-referenced-by-count":0,"title":["Advances in Logic-Based Entity Resolution: Enhancing ASPEN with Local Merges and Optimality Criteria"],"prefix":"10.24963","author":[{"given":"Zhiliang","family":"Xiang","sequence":"first","affiliation":[{"name":"Cardiff University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meghyn","family":"Bienvenu","sequence":"additional","affiliation":[{"name":"LaBRI - CNRS & University of Bordeaux"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gianluca","family":"Cima","sequence":"additional","affiliation":[{"name":"Sapienza University of Rome"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V\u00edctor","family":"Guti\u00e9rrez-Basulto","sequence":"additional","affiliation":[{"name":"Cardiff University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yazm\u00edn","family":"Ib\u00e1\u00f1ez-Garc\u00eda","sequence":"additional","affiliation":[{"name":"Cardiff University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"22nd International Conference on Principles of Knowledge Representation and Reasoning {KR-2025}","theme":"Artificial Intelligence","location":"Melbourne, Australia","acronym":"KR-2025","number":"22","sponsor":["Artificial Intelligence Journal","Principles of Knowledge Representation and Reasoning Inc.","Academic College of Tel-Aviv","European Association for Artificial Intelligence","National Science Foundation"],"start":{"date-parts":[[2025,11,11]]},"end":{"date-parts":[[2025,11,17]]}},"container-title":["Proceedings of the TwentySecond International Conference on Principles of Knowledge Representation and Reasoning"],"original-title":[],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:11:18Z","timestamp":1762323078000},"score":1,"resource":{"primary":{"URL":"https:\/\/proceedings.kr.org\/2025\/64"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,11]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/kr.2025\/64","relation":{},"subject":[],"published":{"date-parts":[[2025,11]]}}}