Lean Kernels in Description Logics

Peñaloza, Rafael; Mencía, Carlos; Ignatiev, Alexey; Marques-Silva, João

doi:10.1007/978-3-319-58068-5_32

Cited by 12 publications

(14 citation statements)

References 29 publications

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“…In essence, these modules are sub-ontologies that contain the union of all justifications. Different techniques balancing the computation time and the quality of the approximation have been proposed [12,13,25,28], but in general the methods based on a syntactic analysis of the ontology tend to behave better.…”

Section: Iar Repairsmentioning

confidence: 99%

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: Iar Repairsmentioning

confidence: 99%

Error-Tolerance and Error Management in Lightweight Description Logics

Peñaloza

2020

Künstl Intell

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“…Our approach is based on the construction of a Horn formula encoding the completion-based procedure for atomic subsumption. The methods proposed generalize to any ontology language with consequence-based reasoning algorithms (Peñaloza et al, 2017). Once the propositional formula is obtained, all optimizations apply.…”

Section: Experimental Evaluationmentioning

confidence: 99%

SATPin: Axiom Pinpointing for Lightweight Description Logics Through Incremental SAT

2020

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One approach to axiom pinpointing (AP) in description logics is its reduction to the enumeration of minimal unsatisfiable subformulas, allowing for the deployment of highly optimized methods from SAT solving. Exploiting the properties of AP, we further optimize incremental SAT solving, resulting in speedups of several orders of magnitude: through persistent incremental solving the solver state is updated lazily when adding clauses or assumptions. This adaptation consistently improves the runtime of the tool by an average factor of 3.8, and a maximum of 38. SATPin, our system, was tested over large biomedical ontologies and performed competitively.

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“…The set of axioms dependent on ¬ψ is in general a superset of the reduced uniform interpolant V * and is referred to as V * app , i.e., an approximation of V * . In this paper, dependency tracing is achieved by using annotations, similar to (Kazakov and Skocovský 2017;Koopmann and Chen 2017;Penaloza et al 2017). These take the form of fresh concept names that do not occur in the signature of the ontology nor the observation.…”

Section: Practical Realisationmentioning

confidence: 99%

ABox Abduction via Forgetting in ALC

Del-Pinto

Schmidt

2019

AAAI

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Abductive reasoning generates explanatory hypotheses for new observations using prior knowledge. This paper investigates the use of forgetting, also known as uniform interpolation, to perform ABox abduction in description logic (ALC) ontologies. Non-abducibles are specified by a forgetting signature which can contain concept, but not role, symbols. The resulting hypotheses are semantically minimal and each consist of a set of disjuncts. These disjuncts are each independent explanations, and are not redundant with respect to the background ontology or the other disjuncts, representing a form of hypothesis space. The observations and hypotheses handled by the method can contain both atomic or complex ALC concepts, excluding role assertions, and are not restricted to Horn clauses. Two approaches to redundancy elimination are explored for practical use: full and approximate. Using a prototype implementation, experiments were performed over a corpus of real world ontologies to investigate the practicality of both approaches across several settings.

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Lean Kernels in Description Logics

Cited by 12 publications

References 29 publications

Error-Tolerance and Error Management in Lightweight Description Logics

Error-Tolerance and Error Management in Lightweight Description Logics

SATPin: Axiom Pinpointing for Lightweight Description Logics Through Incremental SAT

ABox Abduction via Forgetting in ALC

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