BEACON: An Efficient SAT-Based Tool for Debugging $${\mathcal {EL}}{^+}$$ Ontologies

Arif, M. Fareed; Mencía, Carlos; Ignatiev, Alexey; Manthey, Norbert; Peñaloza, Rafael; Marques-Silva, João

doi:10.1007/978-3-319-40970-2_32

Cited by 25 publications

(16 citation statements)

References 21 publications

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“…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].…”

Section: Discussionmentioning

confidence: 92%

Error-Tolerance and Error Management in Lightweight Description Logics

Peñaloza

2020

Künstl Intell

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The construction and maintenance of ontologies is an error-prone task. As such, it is not uncommon to detect unwanted or erroneous consequences in large-scale ontologies which are already deployed in production. While waiting for a corrected version, these ontologies should still be available for use in a "safe" manner, which avoids the known errors. At the same time, the knowledge engineer in charge of producing the new version requires support to explore only the potentially problematic axioms, and reduce the number of exploration steps. In this paper, we explore the problem of deriving meaningful consequences from ontologies which contain known errors. Our work extends the ideas from inconsistency-tolerant reasoning to allow for arbitrary entailments as errors, and allows for any part of the ontology (be it the terminological elements or the facts) to be the causes of the error. Our study shows that, with a few exceptions, tasks related to this kind of reasoning are intractable in general, even for very inexpressive description logics.

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Section: Discussionmentioning

confidence: 92%

Error-Tolerance and Error Management in Lightweight Description Logics

Peñaloza

2020

Künstl Intell

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“…The most obvious and urgent of them is the implementation and evaluation of a BEL reasoner. Towards this goal, we can further optimize the reasoner BORN [18], or extend the methods for axiom-pinpointing that exploit the proof structure [1,2,32] to consider also the probabilistic contexts. In the same direction, we will study further optimizations and special cases where the complexity of reasoning can be further decreased.…”

Section: Discussionmentioning

confidence: 99%

The Bayesian Ontology Language $$\mathcal {BEL}$$ BEL

Ceylan¹,

Peñaloza²

2016

J Autom Reasoning

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We introduce the new probabilistic description logic (DL) BEL, which extends the light-weight DL EL with the possibility of expressing uncertainty about the validity of some knowledge. Contrary to other probabilistic DLs, BEL is designed to represent classical knowledge that depends on an uncertain context; that is, some of the knowledge may hold or not depending on the current situation. The probability distribution of these contexts is expressed by a Bayesian network (BN).We study different reasoning problems in BEL, providing tight complexity bounds for all of them. One particularly interesting property of our framework is that reasoning can be decoupled between the logical (i.e., EL), and the probabilistic (i.e., the BN) components. We later generalize all the notions presented to introduce Bayesian extensions of arbitrary ontology languages. Using the decoupling property, we are able to provide tight complexity bounds for reasoning in the Bayesian extensions of many other DLs. We provide a detailed analysis of our formalism w.r.t. the assumptions made and compare it with the existing approaches.

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“…In order to get a more detailed view on the ability of the different modules to approximate the union of MinAs (UMinAs), we computed UMinAs for all the atomic subsumption relations of GENE and NOT-GALEN using BEA-CON [1]. The results show that LKs match UMinAs for all subsumption relations in GENE, whereas ⊥ * -modules match UMinAs in 95.49% of the cases, being in average around 3.26% larger than UMinAs.…”

Section: Methodsmentioning

confidence: 99%

Lean Kernels in Description Logics

et al. 2017

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Lean kernels (LKs) are an effective optimization for deriving the causes of unsatisfiability of a propositional formula. Interestingly, no analogous notion exists for explaining consequences of description logic (DL) ontologies. We introduce LKs for DLs using a general notion of consequence-based methods, and provide an algorithm for computing them which incurs in only a linear time overhead. As an example, we instantiate our framework to the DL ALC. We prove formally and empirically that LKs provide a tighter approximation of the set of relevant axioms for a consequence than syntactic locality-based modules.

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BEACON: An Efficient SAT-Based Tool for Debugging $${\mathcal {EL}}{^+}$$ Ontologies

Cited by 25 publications

References 21 publications

Error-Tolerance and Error Management in Lightweight Description Logics

Error-Tolerance and Error Management in Lightweight Description Logics

The Bayesian Ontology Language $$\mathcal {BEL}$$ BEL

Lean Kernels in Description Logics

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