- Titel
- Optimal ABox Repair w.r.t. Static EL TBoxes: from Quantified ABoxes back to ABoxes
- Untertitel
- Extended Version
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-785067
- Schriftenreihe
- LTCS-Report
- Bandnummer
- 22-01
- Erstveröffentlichung
- 2022
- DOI
- https://doi.org/10.25368/2022.65
- Abstract (EN)
- Errors in Description Logic (DL) ontologies are often detected when a reasoner computes unwanted consequences. The question is then how to repair the ontology such that the unwanted consequences no longer follow, but as many of the other consequences as possible are preserved. The problem of computing such optimal repairs was addressed in our previous work in the setting where the data (expressed by an ABox) may contain errors, but the schema (expressed by an EL TBox) is assumed to be correct. Actually, we consider a generalization of ABoxes called quantified ABoxes (qABoxes) both as input for and as result of the repair process. Using qABoxes for repair allows us to retain more information, but the disadvantage is that standard DL systems do not accept qABoxes as input. This raises the question, investigated in the present paper, whether and how one can obtain optimal repairs if one restricts the output of the repair process to being ABoxes. In general, such optimal ABox repairs need not exist. Our main contribution is that we show how to decide the existence of optimal ABox repairs in exponential time, and how to compute all such repairs in case they exist.
- Zitiert in
- Optimal ABox Repair w.r.t. Static EL TBoxes: from Quantified ABoxes back to ABoxes
- Freie Schlagwörter (EN)
- Description Logic, Optimal Repair, Quantified ABox, ABox
- Klassifikation (DDC)
- 004
- Klassifikation (RVK)
- ST 136
- Publizierende Institution
- Technische Universität Dresden, Dresden, Germany
- Förder- / Projektangaben
- Deutsche Forschungsgemeinschaft Repairing Description Logic Ontologies
ID: 430150274 - Deutsche Forschungsgemeinschaft TRR 248: Foundations of Perspicuous Software Systems
(cpec)
ID: 389792660 - Center for Scalable Data Analytics and Artificial Intelligence AI competence center ScaDS.AI Dresden/Leipzig
(ScaDS.AI) - Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-785067
- Veröffentlichungsdatum Qucosa
- 17.03.2022
- Dokumenttyp
- Bericht
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0