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François Goasdoué

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6 papers
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6

JAIR Journal 2019 Journal Article

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

  • Meghyn Bienvenu
  • Camille Bourgaux
  • François Goasdoué

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.

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AAAI Conference 2016 Conference Paper

Explaining Inconsistency-Tolerant Query Answering over Description Logic Knowledge Bases

  • Meghyn Bienvenu
  • Camille Bourgaux
  • François Goasdoué

Several inconsistency-tolerant semantics have been introduced for querying inconsistent description logic knowledge bases. This paper addresses the problem of explaining why a tuple is a (non-)answer to a query under such 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 the lightweight description logic 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 propositional satisfiability (SAT), enabling us to generate explanations by exploiting solvers for Boolean satisfaction and optimization problems. Finally, we empirically study the efficiency of our explanation framework using the well-established LUBM benchmark.

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AAAI Conference 2014 Conference Paper

Querying Inconsistent Description Logic Knowledge Bases under Preferred Repair Semantics

  • Meghyn Bienvenu
  • Camille Bourgaux
  • François Goasdoué

Recently several inconsistency-tolerant semantics have been introduced for querying inconsistent description logic knowledge bases. Most of these semantics rely on the notion of a repair, defined as an inclusion-maximal subset of the facts (ABox) which is consistent with the ontology (TBox). In this paper, we study variants of two popular inconsistencytolerant semantics obtained by replacing classical repairs by various types of preferred repair. We analyze the complexity of query answering under the resulting semantics, focusing on the lightweight logic DL-LiteR. Unsurprisingly, query answering is intractable in all cases, but we nonetheless identify one notion of preferred repair, based upon priority levels, whose data complexity is “only” coNP-complete. This leads us to propose an approach combining incomplete tractable methods with calls to a SAT solver. An experimental evaluation of the approach shows good scalability on realistic cases.

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IJCAI Conference 2009 Conference Paper

  • Nada Abdallah
  • François Goasdoué
  • Marie-Christine Rousset

This paper provides a decentralized data model and associated algorithms for peer data management systems (PDMS) based on the DL-LITER description logic. Our approach relies on reducing query reformulation and consistency checking for DL-LITER into reasoning in propositional logic. This enables a straightforward deployment of DL- LITER PDMSs on top of SomeWhere, a scalable propositional peer-to-peer inference system. We also show how to use the state-of-the-art Minicon algorithm for rewriting queries using views in DL- LITER in the centralized and decentralized cases.

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