- Titel
- Reasoning in OWL 2 EL with Hierarchical Concrete Domains
- Untertitel
- Extended Version
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-978686
- Schriftenreihe
- LTCS-Report
- Bandnummer
- 25-04
- Erstveröffentlichung
- 2025
- DOI
- https://doi.org/10.25368/2025.127
- Abstract (EN)
- This is an extended version of an article accepted at the 15th International Symposium on Frontiers of Combining Systems (FroCoS 2025).
- The EL family of description logics facilitates efficient polynomial-time reasoning and has been standardized as the profile OWL 2 EL of the Web Ontology Language. EL can represent and reason not only with symbolic knowledge but also with concrete knowledge expressed by numbers, strings, and other concrete datatypes. Such concrete domains must be convex to avoid introducing disjunctions “through the backdoor.” However, existing concrete domains provide only limited utility. In order to overcome this issue, we introduce a novel form of concrete domains based on semi-lattices. They are convex by design and can thus be integrated into Horn-DLs such as EL. Moreover, they allow for FBoxes to express dependencies between concrete features. We describe four instantiations concerned with real intervals, 2D-polygons, regular languages, and graphs.
- Zitiert in
- Proceedings of the 15th International Symposium on Frontiers of Combining Systems (FroCoS 2025)
- Freie Schlagwörter (DE)
- Beschreibungslogik, Konkreter Bereich, Halbverband, Intervall, Polygon, Reguläre Sprache, Graph
- Freie Schlagwörter (EN)
- Description logic, Concrete domain, Semi-lattice, Interval, Polygon, Regular language, Graph
- Klassifikation (DDC)
- 004
- Klassifikation (RVK)
- ST 136
- Herausgeber (Institution)
- Technische Universität Dresden
- Förder- / Projektangaben
- Deutsche Forschungsgemeinschaft (DFG)
TRR 248: Foundations of Perspicuous Software Systems - Enabling Comprehension in a Cyber-Physical World
ID: 389792660 - Deutsche Forschungsgemeinschaft (DFG)
Construction and Repair of Description-logic Knowledge Bases
ID: 558917076 - Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig (ScaDS.AI)
- Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-978686
- Veröffentlichungsdatum Qucosa
- 09.07.2025
- Dokumenttyp
- Bericht
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0