{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T07:00:07Z","timestamp":1762326007084,"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>ASPIC+ is one of the main general frameworks for rule-based argumentation for AI. Although first-order rules are commonly used in ASPIC+ examples, most existing approaches to reason over rule-based argumentation only support propositional rules. To enable reasoning over first-order instances, a preliminary grounding step is required. As groundings can lead to an exponential increase in the size of the input theories, intelligent procedures are needed. However, there is a lack of dedicated solutions for ASPIC+. Therefore, we propose an intelligent grounding procedure that keeps the size of the grounding manageable while preserving the correctness of the reasoning process. To this end, we translate the first-order ASPIC+ instance into a Datalog program and query a Datalog engine to obtain ground substitutions to perform the grounding of rules and contraries. Additionally, we propose simplifications specific to the ASPIC+ formalism to avoid grounding of rules that have no influence on the reasoning process. Finally, we performed an empirical evaluation of a prototypical implementation to show scalability.<\/jats:p>","DOI":"10.24963\/kr.2025\/28","type":"proceedings-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:10:44Z","timestamp":1762323044000},"page":"281-292","source":"Crossref","is-referenced-by-count":0,"title":["Grounding Rule-Based Argumentation Using Datalog"],"prefix":"10.24963","author":[{"given":"Martin","family":"Diller","sequence":"first","affiliation":[{"name":"Logic Programming and Argumentation Group, TU Dresden, Germany"}]},{"given":"Sarah Alice","family":"Gaggl","sequence":"additional","affiliation":[{"name":"Logic Programming and Argumentation Group, TU Dresden, Germany"}]},{"given":"Philipp","family":"Hanisch","sequence":"additional","affiliation":[{"name":"Knowledge-Based Systems Group, TU Dresden, Germany"}]},{"given":"Giuseppina","family":"Monterosso","sequence":"additional","affiliation":[{"name":"DIMES - University of Calabria, Italy"}]},{"given":"Fritz","family":"Rauschenbach","sequence":"additional","affiliation":[{"name":"Logic Programming and Argumentation Group, TU Dresden, Germany"}]}],"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":"2025-11-05T06:11:04Z","timestamp":1762323064000},"score":1,"resource":{"primary":{"URL":"https:\/\/proceedings.kr.org\/2025\/28"}},"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\/28","relation":{},"subject":[],"published":{"date-parts":[[2025,11]]}}}