Inconsistency-tolerant query answering in ontology-based data access

Abstract

a b s t r a c tOntology-based data access (OBDA) is receiving great attention as a new paradigm for managing information systems through semantic technologies. According to this paradigm, a Description Logic ontology provides an abstract and formal representation of the domain of interest to the information system, and is used as a sophisticated schema for accessing the data and formulating queries over them. In this paper, we address the problem of dealing with inconsistencies in OBDA. Our general goal is both to study DL semantical frameworks that are inconsistency-tolerant, and to devise techniques for answering unions of conjunctive queries under such inconsistency-tolerant semantics. Our work is inspired by the approaches to consistent query answering in databases, which are based on the idea of living with inconsistencies in the database, but trying to obtain only consistent information during query answering, by relying on the notion of database repair. We first adapt the notion of database repair to our context, and show that, according to such a notion, inconsistency-tolerant query answering is intractable, even for very simple DLs. Therefore, we propose a different repair-based semantics, with the goal of reaching a good compromise between the expressive power of the semantics and the computational complexity of inconsistency-tolerant query answering. Indeed, we show that query answering under the new semantics is first-order rewritable in OBDA, even if the ontology is expressed in one of the most expressive members of the DL-Lite family.

Acquiring Ontology Axioms through Mappings to Data Sources

“…As for consistency, an interesting idea is to allow for contradicting TBox axioms coming to the data sources to coexist as long as they are not used together when performing query answering. Other promising directions to pursue include handling possible inconsistencies in MKBs, in the spirit of [28], considering more sophisticated form of mappings, such as the ones in the context of peer-to-peer data integration [29], conceiving the assertions coming from the mapping as updates on the current knowledge base (in the spirit of [30]), and exploiting the ideas presented here as a basis for the problem of acquiring knowledge graphs (see [31]) from existing data sources.…”

Ontology-mediated query answering over temporal and inconsistent data

“…A prominent approach for inconsistency-tolerant reasoning is to consider repairs of the knowledge base, i.e., maximal consistent subsets of the data, and then to perform query answering with respect to these subsets. Arguably the most natural and well-known inconsistency-tolerant semantics is the AR semantics [29,30], inspired by consistent query answering in the database setting [31], which considers the queries that hold in every repair. However, AR query answering is intractable even for very simple ontologies [32], which leads the authors of [29,30] to propose an approximation of AR tractable for DL-Lite R , namely the IAR semantics, which queries the intersection of the repairs.…”

“…Arguably the most natural and well-known inconsistency-tolerant semantics is the AR semantics [29,30], inspired by consistent query answering in the database setting [31], which considers the queries that hold in every repair. However, AR query answering is intractable even for very simple ontologies [32], which leads the authors of [29,30] to propose an approximation of AR tractable for DL-Lite R , namely the IAR semantics, which queries the intersection of the repairs. Beside its better computational properties, this semantics is more cautious, since it provides answers supported by facts that are not involved in any contradictions.…”

“…For the two DLs to be investigated in this paper, answering of (atemporal) conjunctive queries under these inconsistency-tolerant semantics has already been investigated for DL-Lite R in [29,30,33,34] and for EL ⊥ in [28,35]. Attention has then turned to the problem of designing algorithms and implementing these alternative semantics.…”

“…Attention has then turned to the problem of designing algorithms and implementing these alternative semantics. Most work has focused on the IAR semantics and dialects of DL-Lite, due to the aforementioned tractability result [30,36,37]. A notable exception is the CQAPri system, which implements all three mentioned semantics-AR, IAR and brave-for DL-Lite R knowledge bases [38,39].…”