93 research outputs found
Get my pizza right: Repairing missing is-a relations in ALC ontologies (extended version)
With the increased use of ontologies in semantically-enabled applications,
the issue of debugging defects in ontologies has become increasingly important.
These defects can lead to wrong or incomplete results for the applications.
Debugging consists of the phases of detection and repairing. In this paper we
focus on the repairing phase of a particular kind of defects, i.e. the missing
relations in the is-a hierarchy. Previous work has dealt with the case of
taxonomies. In this work we extend the scope to deal with ALC ontologies that
can be represented using acyclic terminologies. We present algorithms and
discuss a system
Reasoning about Explanations for Negative Query Answers in DL-Lite
In order to meet usability requirements, most logic-based applications
provide explanation facilities for reasoning services. This holds also for
Description Logics, where research has focused on the explanation of both TBox
reasoning and, more recently, query answering. Besides explaining the presence
of a tuple in a query answer, it is important to explain also why a given tuple
is missing. We address the latter problem for instance and conjunctive query
answering over DL-Lite ontologies by adopting abductive reasoning; that is, we
look for additions to the ABox that force a given tuple to be in the result. As
reasoning tasks we consider existence and recognition of an explanation, and
relevance and necessity of a given assertion for an explanation. We
characterize the computational complexity of these problems for arbitrary,
subset minimal, and cardinality minimal explanations
Connection-Minimal Abduction in via Translation to {FOL}
International audienceAbduction in description logics finds extensions of a knowledge base to make it entail an observation. As such, it can be used to explain why the observation does not follow, to repair incomplete knowledge bases, and to provide possible explanations for unexpected observations. We consider TBox abduction in the lightweight description logic EL , where the observation is a concept inclusion and the background knowledge is a TBox, i.e., a set of concept inclusions. To avoid useless answers, such problems usually come with further restrictions on the solution space and/or minimality criteria that help sort the chaff from the grain. We argue that existing minimality notions are insufficient, and introduce connection minimality. This criterion follows Occam’s razor by rejecting hypotheses that use concept inclusions unrelated to the problem at hand. We show how to compute a special class of connection-minimal hypotheses in a sound and complete way. Our technique is based on a translation to first-order logic, and constructs hypotheses based on prime implicates. We evaluate a prototype implementation of our approach on ontologies from the medical domain
Why Not? Explaining Missing Entailments with Evee (Technical Report)
Understanding logical entailments derived by a description logic reasoner is
not always straight-forward for ontology users. For this reason, various
methods for explaining entailments using justifications and proofs have been
developed and implemented as plug-ins for the ontology editor Prot\'eg\'e.
However, when the user expects a missing consequence to hold, it is equally
important to explain why it does not follow from the ontology. In this paper,
we describe a new version of , a Prot\'eg\'e plugin
that now also provides explanations for missing consequences, via existing and
new techniques based on abduction and counterexamples
Two Ways of Explaining Negative Entailments in Description Logics Using Abduction: Extended Version
We discuss two ways of using abduction to explain missing entailments from description logic knowledge bases, one more common, one more unusual, and then have a closer look at how current results/implementations on abduction could be used towards generating such explanations, and what still needs to be done.This is an extended version of an article submitted to XLoKR 2021
Abduction in {EL} via Translation to {FOL}
International audienceWe present a technique for performing TBox abduction in the description logic EL. The input problem is converted into first-order formulas on which a prime implicate generation technique is applied, then EL hypotheses are reconstructed by combining the generated positive and negative implicates
Heuristic Ranking in Tightly Coupled Probabilistic Description Logics
The Semantic Web effort has steadily been gaining traction in the recent
years. In particular,Web search companies are recently realizing that their
products need to evolve towards having richer semantic search capabilities.
Description logics (DLs) have been adopted as the formal underpinnings for
Semantic Web languages used in describing ontologies. Reasoning under
uncertainty has recently taken a leading role in this arena, given the nature
of data found on theWeb. In this paper, we present a probabilistic extension of
the DL EL++ (which underlies the OWL2 EL profile) using Markov logic networks
(MLNs) as probabilistic semantics. This extension is tightly coupled, meaning
that probabilistic annotations in formulas can refer to objects in the
ontology. We show that, even though the tightly coupled nature of our language
means that many basic operations are data-intractable, we can leverage a
sublanguage of MLNs that allows to rank the atomic consequences of an ontology
relative to their probability values (called ranking queries) even when these
values are not fully computed. We present an anytime algorithm to answer
ranking queries, and provide an upper bound on the error that it incurs, as
well as a criterion to decide when results are guaranteed to be correct.Comment: Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty
in Artificial Intelligence (UAI2012
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