Debugging Incoherent Terminologies

Schlobach, Stefan; Huang, Zhisheng; Cornet, Ronald; Harmelen, Frank van

doi:10.1007/s10817-007-9076-z

Cited by 150 publications

(123 citation statements)

References 15 publications

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“…A black-box approach to the computation of MUPSes is presented in (Schlobach et al 2007). Since it makes use of an external DL reasoner, it can deal with any class of terminology for which a reasoner exists.…”

Section: Explanations In Description Logicsmentioning

confidence: 99%

AuRUS: explaining the validation of UML/OCL conceptual schemas

et al. 2013

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Abstract:The validation and the verification of conceptual schemas have attracted a lot of interest during the last years, and several tools have been developed to automate this process as much as possible. This is achieved, in general, by assessing whether the schema satisfies different kinds of desirable properties which ensure that the schema is correct. In this paper we describe AuRUS, a tool we have developed to analyze UML/OCL conceptual schemas and to explain their (in)correctness. When a property is satisfied, AuRUS provides a sample instantiation of the schema showing a particular situation where the property holds. When it is not, AuRUS provides an explanation for such unsatisfiability, i.e., a set of integrity constraints which is in contradiction with the property.

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Section: Explanations In Description Logicsmentioning

confidence: 99%

AuRUS: explaining the validation of UML/OCL conceptual schemas

et al. 2013

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“…Most of the modern ontology diagnosis methods [6,7,8,9] are implemented according to Proposition 2 and differ in details, e.g. how and when (minimal) conflict sets are computed, the order in which hitting sets are generated, etc.…”

Section: Ontology Entailmentsmentioning

confidence: 99%

“…Diagnosis methods for ontologies are introduced in [6,7,8]. Ranking of diagnoses and proposing a target diagnosis is presented in [10].…”

Section: Related Workmentioning

confidence: 99%

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Query Strategy for Sequential Ontology Debugging

Shchekotykhin

Friedrich

2010

Lecture Notes in Computer Science

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Debugging of ontologies is an important prerequisite for their wide-spread application, especially in areas that rely upon everyday users to create and maintain knowledge bases, as in the case of the Semantic Web. Recent approaches use diagnosis methods to identify causes of inconsistent or incoherent ontologies. However, in most debugging scenarios these methods return many alternative diagnoses, thus placing the burden of fault localization on the user. This paper demonstrates how the target diagnosis can be identified by performing a sequence of observations, that is, by querying an oracle about entailments of the target ontology. We exploit a-priori probabilities of typical user errors to formulate information-theoretic concepts for query selection. Our evaluation showed that the proposed method significantly reduces the number of required queries compared to myopic strategies. We experimented with different probability distributions of user errors and different qualities of the a-priori probabilities. Our measurements showed the advantageousness of information-theoretic approach to query selection even in cases where only a rough estimate of the priors is available.

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“…For example, some researchers proposed methods to debug erroneous terminologies and have them repaired when inconsistencies are detected [17,18,13,16,11,4]. Some researchers considered resolving the logical inconsistencies during ontology evolution through some revision operators (see, for example, [15]).…”

Section: The State Of Artmentioning

confidence: 99%

Dealing with Inconsistencies in DL-Lite Ontologies

Zhou

2009

Lecture Notes in Computer Science

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The Research ProblemAs a shared conceptualization of a particular domain, ontologies play an important role for the success of the Semantic Web. However it is often difficult to create an absolutely consistent ontology. Inconsistency can occur due to several reasons, such as modeling errors, migration or merging ontologies, and ontology evolution. So it is essential to study how to deal with inconsistent ontologies. Many approaches have been proposed to solve this problem. These approaches are mainly used for dealing with inconsistency in expressive Description Logics(DLs). In our work, we consider inconsistency handling in the DL-Lite family, which is a family of DLs that preserve tractable reasoning and are specifically tailored to deal with large amounts of data. Like other DLs, inconsistencies in DL-Lite can also easily occur because disjoint axioms are allowed.

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Debugging Incoherent Terminologies

Cited by 150 publications

References 15 publications

AuRUS: explaining the validation of UML/OCL conceptual schemas

AuRUS: explaining the validation of UML/OCL conceptual schemas

Query Strategy for Sequential Ontology Debugging

Dealing with Inconsistencies in DL-Lite Ontologies

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