Inconsistency-Tolerant Semantics for Description Logics

Lembo, Domenico; Lenzerini, Maurizio; Rosati, Riccardo; Ruzzi, Marco; Savo, Domenico Fabio

doi:10.1007/978-3-642-15918-3_9

Cited by 133 publications

(217 citation statements)

References 10 publications

Supporting

Mentioning

209

Contrasting

Unclassified

Order By: Relevance

“…An inconsistency-tolerant semantics in the context of OBDA was first investigated in [30]; this semantics is based on the notion of AR-repairs (ABox-repairs) and has become known as AR-semantics. Subsequent investigations of AR-semantics were carried out in a number of papers, including (in chronological order) [29,37,9,11,10,33]. These papers have analyzed the computational complexity of consistent query answering in OBDA and have also considered several variants of the AR-semantics in the OBDA framework.…”

Section: Connections With Ontology-based Data Accessmentioning

confidence: 99%

Exchange-Repairs: Managing Inconsistency in Data Exchange

Cate

Halpert

Kolaitis

2014

Web Reasoning and Rule Systems

View full text Add to dashboard Cite

In a data exchange setting with target constraints, it is often the case that a given source instance has no solutions. Intuitively, this happens when data sources contain inconsistent or conflicting information that is exposed by the target constraints at hand. In such cases, the semantics of target queries trivialize, because the certain answers of every target query over the given source instance evaluate to "true". The aim of this paper is to introduce and explore a new framework that gives meaningful semantics in such cases by using the notion of exchange-repairs. Informally, an exchange-repair of a source instance is another source instance that differs minimally from the first, but has a solution. In turn, exchange-repairs give rise to a natural notion of exchange-repair certain answers (in short, XR-certain answers) for target queries in the context of data exchange with target constraints.After exploring the structural properties of exchange-repairs, we focus on the problem of computing the XR-certain answers of conjunctive queries. We show that for schema mappings specified by source-to-target GAV dependencies and target equality-generating dependencies (egds), the XRcertain answers of a target conjunctive query can be rewritten as the consistent answers (in the sense of standard database repairs) of a union of conjunctive queries over the source schema with respect to a set of egds over the source schema, thus making it possible to use a consistent query-answering system to compute XR-certain answers in data exchange. In contrast, we show that this type of rewriting is not possible for schema mappings specified by source-to-target LAV dependencies and target egds, nor for schema mappings specified by both source-to-target and target GAV dependencies. We then examine the general case of schema mappings specified by source-to-target GLAV constraints, a weakly acyclic set of target tgds and a set of target egds. The main result asserts that, for such settings, the XR-certain answers of conjunctive queries can be rewritten as the certain answers of a union of conjunctive queries with respect to the stable models of a disjunctive logic program over a suitable expansion of the source schema.

show abstract

Section: Connections With Ontology-based Data Accessmentioning

confidence: 99%

Exchange-Repairs: Managing Inconsistency in Data Exchange

Cate

Halpert

Kolaitis

2014

Web Reasoning and Rule Systems

View full text Add to dashboard Cite

show abstract

“…Due to space limitations, we refer the reader to [7] for details on the semantics of DL-Lite A . We then briefly recall the IAR semantics for inconsistency-tolerance in DL ontologies (see [5] for more details). Let K = T , A be a DL ontology.…”

Section: Abox Cleaning Technique For Owl Qlmentioning

confidence: 99%

“…In particular, we consider inconsistency-tolerant semantics for general DLs recently proposed in [5], called IAR semantics, for which reasoning has been studied in the context of the Description Logics of the DL-Lite family, and in particular the DL DL-Lite A , that underlies the OWL profile OWL 2 QL. The IAR semantics is centered around the notion of ABox repair, which is a very simple and natural one: the ABox repair of a DL ontology is the intersection of all the maximal subsets of the ABox that are consistent with the TBox.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, in [5] it was proved that computing the ABox repair of a DL-Lite A ontology under the IAR semantics is a tractable problem. Then, in [6] a technique for query answering under IAR-semantics in DL-Lite A is presented: instead of modifying the ABox, this method is based on computing a rewriting Q of the initial query Q and then evaluating the query Q with respect to the original ABox.…”

Section: Introductionmentioning

confidence: 99%

“…We argue that the results of [5,6] are potentially very important from the practical viewpoint, for the following reasons: (i) they are based on formally grounded notions of inconsistency-tolerant semantics; (ii) they identify (to the best of our knowledge) the first inconsistency-tolerant semantics in DLs for which query answering is tractable. So, based on such results, in principle it might be possible to define practical algorithms for handling inconsistency in OWL 2 QL.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of Techniques for Inconsistency Handling in OWL 2 QL Ontologies

Rosati

Ruzzi

Graziosi

et al. 2012

The Semantic Web – ISWC 2012

Self Cite

View full text Add to dashboard Cite

Abstract. In this paper we present the Quonto Inconsistent Data handler (QuID). QuID is a reasoner for OWL 2 QL that is based on the system Quonto and is able to deal with inconsistent ontologies. The central aspect of QuID is that it implements two different, orthogonal strategies for dealing with inconsistency: ABox repairing techniques, based on data manipulation, and consistent query answering techniques, based on query rewriting. Moreover, by exploiting the ability of Quonto to delegate the management of the ABox to a relational database system (DBMS), such techniques are potentially able to handle very large inconsistent ABoxes. For the above reasons, QuID allows for experimentally comparing the above two different strategies for inconsistency handling in the context of OWL 2 QL. We thus report on the experimental evaluation that we have conducted using QuID. Our results clearly point out that inconsistency-tolerance in OWL 2 QL ontologies is feasible in practical cases. Moreover, our evaluation singles out the different sources of complexity for the data manipulation technique and the query rewriting technique, and allows for identifying the conditions under which one method is more efficient than the other.

show abstract

Multi-criteria Decision Making with Existential Rules Using Repair Techniques

Karanikolas

Croitoru

Bisquert

et al. 2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

The central problem in multi-criteria decision making is to reach an acceptable decision aggregating preferences over multiple criteria. In this paper, we explain how to benefit from the reasoning capabilities of existential rules for modelling a multi-criteria decision making problem as an inconsistent knowledge base. The repairs of this knowledge base represent the maximally consistent point of views and inference strategies can be used for decision making.

show abstract

Inconsistency-Tolerant Semantics for Description Logics

Cited by 133 publications

References 10 publications

Exchange-Repairs: Managing Inconsistency in Data Exchange

Exchange-Repairs: Managing Inconsistency in Data Exchange

Evaluation of Techniques for Inconsistency Handling in OWL 2 QL Ontologies

Multi-criteria Decision Making with Existential Rules Using Repair Techniques

Contact Info

Product

Resources

About