{"type":"ScholarlyArticle","title":"Finding Justifications by Approximating Core for Large-scale Ontologies","authors":[{"name":"Mengyu Gao","slug":"mengyu-gao","given":"Mengyu","family":"Gao","affiliation":"College of Computer Science and Technology , Jilin University , Changchun 130012"},{"name":"Yuxin Ye","slug":"yuxin-ye","given":"Yuxin","family":"Ye","affiliation":"College of Computer Science and Technology , Jilin University , Changchun 130012; Key Laboratory of Symbol Computation and Knowledge Engineering ( Jilin University) , Ministry of Education , Changchun 130012 gaomengyu"},{"name":"Dantong Ouyang","slug":"dantong-ouyang","given":"Dantong","family":"Ouyang","affiliation":"College of Computer Science and Technology , Jilin University , Changchun 130012; Key Laboratory of Symbol Computation and Knowledge Engineering ( Jilin University) , Ministry of Education , Changchun 130012 gaomengyu"},{"name":"Bin Wang","slug":"bin-wang","given":"Bin","family":"Wang","affiliation":"College of Computer Science and Technology , Jilin University , Changchun 130012"}],"abstract":"Finding justifications for an entailment is one of the major missions in the field of ontology research. Recent advances on finding justifications w.r.t. the light-weight description logics focused on encoding this problem into a propositional formula, and using SAT-based techniques to enumerate all MUSes (minimally unsatisfiable subformulas). It's necessary to import more optimized techniques into finding justifications as emergence of large-scale real-world ontologies. In this paper, we propose a new strategy which introduce local search(in short, LS) technique to compute the approximating core before extracting an exact MUS. Although it is based on a heuristic and LS, such technique is complete in the sense that it always delivers a MUS for any unsatisfiable SAT instance. Our method will find the justifications for large-scale ontologies more effectively.","doi":"10.24963/ijcai.2019/905","url":"https://ijcai.org/proceedings/2019/905","pdf":"https://ijcai.org/proceedings/2019/0905.pdf","markdown":"https://ijcai.org/proceedings/2019/905.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/905","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":905,"pages":{"first":6432,"last":6433},"keywords":["Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving","Knowledge Representation and Reasoning: Description Logics and Ontologies","Constraints and SAT: SAT: Applications"],"videos":[],"references":[{"position":1,"raw":"[Arif et al., 2015] M. 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