Abstract
We consider the well-known setting of ontology-based query answering.
In real-life applications, involving large amount of data, it is possible
that the data are inconsistent with the ontology. Since standard ontology
languages adhere to the classical first-order logic semantics, inconsistencies
are nothing else than logical contradictions. Therefore, the classical
inference semantics fails terribly when faced an inconsistency, since everything
is inferred from a contradiction. Querying inconsistent knowledge bases is an intriguing new problem that gave rise to a flourishing research activity in the KR community. In this talk, we focus on rule-based ontology languages, and we demonstrate the tight connection between classical and consistent query answering. More precisely, we focus on the standard inconsistency-tolerant semantics, namely, the ABox repair (AR) semantics, and we establish generic complexity results that allow us to obtain in a uniform way a relatively complete picture of the complexity of our problem. We also discuss sound approximations of the AR semantics, with the aim of achieving tractability of consistent query answering in data complexity
In real-life applications, involving large amount of data, it is possible
that the data are inconsistent with the ontology. Since standard ontology
languages adhere to the classical first-order logic semantics, inconsistencies
are nothing else than logical contradictions. Therefore, the classical
inference semantics fails terribly when faced an inconsistency, since everything
is inferred from a contradiction. Querying inconsistent knowledge bases is an intriguing new problem that gave rise to a flourishing research activity in the KR community. In this talk, we focus on rule-based ontology languages, and we demonstrate the tight connection between classical and consistent query answering. More precisely, we focus on the standard inconsistency-tolerant semantics, namely, the ABox repair (AR) semantics, and we establish generic complexity results that allow us to obtain in a uniform way a relatively complete picture of the complexity of our problem. We also discuss sound approximations of the AR semantics, with the aim of achieving tractability of consistent query answering in data complexity
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Joint Ontology Workshops 2015 Episode 1: The Argentine Winter of Ontology co-located with the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015), Buenos Aires, Argentina, July 25-27, 2015. |
| Publisher | CEUR Workshop Proceedings (CEUR-WS.org) |
| Number of pages | 1 |
| Publication status | Published - 2015 |