Computing and Explaining Query Answers over Inconsistent DL-Lite Knowledge Bases

Abstract


 
 
 Several inconsistency-tolerant semantics have been introduced for querying inconsistent description logic knowledge bases. The first contribution of this paper is a practical approach for computing the query answers under three well-known such semantics, namely the AR, IAR and brave semantics, in the lightweight description logic DL-LiteR. We show that query answering under the intractable AR semantics can be performed efficiently by using IAR and brave semantics as tractable approximations and encoding the AR entailment problem as a propositional satisfiability (SAT) problem. The second issue tackled in this work is explaining why a tuple is a (non-)answer to a query under these semantics. We define explanations for positive and negative answers under the brave, AR and IAR semantics. We then study the computational properties of explanations in DL-LiteR. For each type of explanation, we analyze the data complexity of recognizing (preferred) explanations and deciding if a given assertion is relevant or necessary. We establish tight connections between intractable explanation problems and variants of SAT, enabling us to generate explanations by exploiting solvers for Boolean satisfaction and optimization problems. Finally, we empirically study the efficiency of our query answering and explanation framework using a benchmark we built upon the well-established LUBM benchmark.
 
 

Error-Tolerance and Error Management in Lightweight Description Logics

“…One potential approach to achieve this is to exploit the properties of very efficient SAT solvers. We note that SAT-based techniques have already shown promising results in the areas of axiom pinpointing and inconsistent query answering [1,10,27].…”

Lexicographic Repair Under Querying Prioritised DL-Lite Knowledge Bases

“…The works that deal with propositional setting, (Benferhat et al, 1995) where the authors studied the function of priorities in the management of inconsistency within the framework of sentential logic by incorporating acceptable relationships with implications capable of inferring non-trivial assumptions; 2. The works that deal with different semantics, presented essentially in the works of Trivela et al (2019), Bienvenu et al (2019) and Dixit (2019). Trivela et al (2019) suggest a general structure that takes into account the semantics of Intersection Closed ABox Repair (ICAR).…”

“…Trivela et al (2019) suggest a general structure that takes into account the semantics of Intersection Closed ABox Repair (ICAR). Bienvenu et al (2019) contribute a practical approach for computing the query answers under three semantics, ABox Repair (AR), Intersection ABox Repair (IAR) and brave semantics in the lightweight description logic DL-Lite R . However, Dixit (2019) suggests Consistent…”

A Short Survey on Inconsistency Handling in Ontology-Mediated Query Answering

“…CQAPri system [14,17] This system 4 computes answers to CQs over DL-Lite R KBs under the IAR, brave, and AR semantics (as well as prioritized versions of AR and IAR). Answers are first computed for the IAR and brave semantics, by evaluating a UCQ-rewriting and filtering the results using a pre-computed set of minimal T -inconsistent subsets.…”