{"type":"ScholarlyArticle","title":"Efficient Computation of General Modules for ALC Ontologies","authors":[{"name":"Hui Yang","slug":"hui-yang","given":"Hui","family":"Yang","affiliation":"LISN , CNRS , Université Paris-Saclay"},{"name":"Patrick Koopmann","slug":"patrick-koopmann","given":"Patrick","family":"Koopmann","affiliation":"Vrije Universiteit Amsterdam , The Netherlands"},{"name":"Yue Ma","slug":"yue-ma","given":"Yue","family":"Ma","affiliation":"LISN , CNRS , Université Paris-Saclay"},{"name":"Nicole Bidoit","slug":"nicole-bidoit","given":"Nicole","family":"Bidoit","affiliation":"LISN , CNRS , Université Paris-Saclay"}],"abstract":"We present a method for extracting general modules for ontologies formulated in the description logic ALC. A module for an ontology is an ideally substantially smaller ontology that preserves all entailments for a user-specified set of terms. As such, it has applications such as ontology reuse and ontology analysis. Different from classical modules, general modules may use axioms not explicitly present in the input ontology, which allows for additional conciseness. So far, general modules have only been investigated for lightweight description logics.\r\nWe present the first work that considers the more expressive description logic ALC. In particular, our contribution is a new method based on uniform interpolation supported by some new theoretical results. Our evaluation indicates that our general modules are often smaller than classical modules and uniform interpolants computed by the state-of-the-art, and compared with uniform interpolants, can be computed in significantly shorter time. Moreover, our method can be used for, and in fact, improves the computation of uniform interpolants and classical modules.","doi":"10.24963/ijcai.2023/374","url":"https://ijcai.org/proceedings/2023/374","pdf":"https://ijcai.org/proceedings/2023/0374.pdf","markdown":"https://ijcai.org/proceedings/2023/374.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/374","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":374,"pages":{"first":3356,"last":3364},"keywords":["Knowledge Representation and Reasoning: KRR: Description logics and ontologies"],"videos":[],"references":[{"position":1,"raw":"[Alghamdi et al., 2021] Ghadah Alghamdi, Renate A. 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