A Comprehensive Framework for Controlled Query Evaluation, Consistent Query Answering and KB Updates in Description Logics

In this extended abstract we discuss the relationship between confidentiality-preserving frameworks and inconsistency-tolerant repair and update semantics in Description Logics (DL). In particular, we consider the well-known problems of Consistent Query Answering, Controlled Query Evaluation, and Knowledge Base Update in DL and introduce a unifying framework that can be naturally instantiated to capture significant settings for the above problems, previously investigated in the literature

Repairing Ontologies via Axiom Weakening.

Ontology engineering is a hard and error-prone task, in which small changes may lead to errors, or even produce an inconsistent ontology. As ontologies grow in size, the need for automated methods for repairing inconsistencies while preserving as much of the original knowledge as possible increases. Most previous approaches to this task are based on removing a few axioms from the ontology to regain consistency. We propose a new method based on weakening these axioms to make them less restrictive, employing the use of refinement operators. We introduce the theoretical framework for weakening DL ontologies, propose algorithms to repair ontologies based on the framework, and provide an analysis of the computational complexity. Through an empirical analysis made over real-life ontologies, we show that our approach preserves significantly more of the original knowledge of the ontology than removing axioms

Complexity of Approximate Query Answering under Inconsistency in Datalog+/-

This is the author accepted manuscript. The final version is available from the publisher via the link in this recordSeveral semantics have been proposed to query inconsistent ontological knowledge bases, including the intersection of repairs and the intersection of closed repairs as two approximate inconsistency-tolerant semantics. In this paper, we analyze the complexity of conjunctive query answering under these two semantics for a wide range of Datalog± languages. We consider both the standard setting, where errors may only be in the database, and the generalized setting, where also the rules of a Datalog± knowledge base may be erroneous.This work was supported by The Alan Turing Institute under the UK EPSRC grant EP/N510129/1, and by the EPSRC grants EP/R013667/1, EP/L012138/1, and EP/M025268/1

Explaining Query Answers under Inconsistency-Tolerant Semantics over Description Logic Knowledge Bases (Extended Abstract)

The problem of querying description logic (DL) knowledge bases (KBs) using database-style queries (in particular, conjunctive queries) has been a major focus of recent DL research. Since scalability is a key concern, much of the work has focused on lightweight DLs for which query answering can be performed in polynomial time w.r.t. the size of the ABox. The DL-Lite family of lightweight DLs [10] is especially popular due to the fact that query answering can be reduced, via query rewriting, to the problem of standard database query evaluation. Since the TBox is usually developed by experts and subject to extensive debugging, it is often reasonable to assume that its contents are correct. By contrast, the ABox is typically substantially larger and subject to frequent modifications, making errors almost inevitable. As such errors may render the KB inconsistent, several inconsistency-tolerant semantics have been introduced in order to provide meaningful answers to queries posed over inconsistent KBs. Arguably the most well-known is the AR semantics [17], inspired by work on consistent query answering in databases (cf. [4] for a survey). Query answering under AR semantics amounts to considering those answers (w.r.t. standard semantics) that can be obtained from every repair, the latter being defined as an inclusion-maximal subset of the ABox that is consistent with the TBox. A more cautious semantics, called IAR semantics The need to equip reasoning systems with explanation services is widely acknowledged by the DL community. Indeed, there have been numerous works on axiom pinpointing, in which the objective is to identify (minimal) subsets of a KB that entail a given TBox axiom (or ABox assertion

Towards Parallel Repair Using Decompositions

Ontology repair remains one of the main bottlenecks for the development of ontologies for practical use. Many automated methods have been developed for suggesting potential repairs, but ultimately human intervention is required for selecting the adequate one, and the human expert might be overwhelmed by the amount of information delivered to her. We propose a decomposition of ontologies into smaller components that can be repaired in parallel. We show the utility of our approach for ontology repair, provide algorithms for computing this decomposition through standard reasoning, and study the complexity of several associated problems

Inconsistency Handling in Ontology-Mediated Query Answering: A Progress Report

International audienceThis paper accompanies an invited talk on inconsistency handling in OMQA and presents a concise summary of the research that has been conducted in the area

A General Modifier-based Framework for Inconsistency-Tolerant Query Answering

We propose a general framework for inconsistency-tolerant query answering within existential rule setting. This framework unifies the main semantics proposed by the state of art and introduces new ones based on cardinality and majority principles. It relies on two key notions: modifiers and inference strategies. An inconsistency-tolerant semantics is seen as a composite modifier plus an inference strategy. We compare the obtained semantics from a productivity point of view