Measuring Inconsistency for Description Logics Based on Paraconsistent Semantics

Ma, Yue; Qi, Guilin; Hitzler, Pascal; Lin, Zuoquan

doi:10.1007/978-3-540-75256-1_6

Cited by 24 publications

(32 citation statements)

References 15 publications

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“…For example, users can input some DL-Lite ontologies and obtain the inconsistency degree of each ontology; users can either resolve inconsistency if the degree is high (e.g. greater than 0.7 [10]) or ignore it.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, some researchers provided methods to measure the incoherence [14] or inconsistency [10,9,3] axioms need to be removed or modified to resolve an inconsistency (see [3,10]). Qi and Hunter in [14] provide a method for measuring the incoherence of an ontology.…”

Section: The State Of Artmentioning

confidence: 99%

“…We expect that the inconsistency degree of an ontology is a single value. Unlike the work given in [10], our work uses a single value as the inconsistency degree of an ontology. Arguably, this single value is easier to be used to deal with inconsistencies, we will find an algorithm to compute the inconsistency degree in polynomial time.…”

Section: Proposed Approachmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: The State Of Artmentioning

confidence: 99%

Section: Proposed Approachmentioning

confidence: 99%

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Dealing with Inconsistencies in DL-Lite Ontologies

Zhou

2009

Lecture Notes in Computer Science

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“…Recently, some approaches for measuring inconsistency in an ontology have been proposed [3,12]. In [3], some inconsistency measures are proposed by adapting the approach based on Shapley values in [7].…”

Section: Related Workmentioning

confidence: 99%

“…In [3], some inconsistency measures are proposed by adapting the approach based on Shapley values in [7]. The approach given in [12] is defined by a four-valued semantics of DL ALC. Our approaches differ from these approaches in that we consider measuring incoherence of an ontology instead of inconsistency, although the measures of incoherence implicitly provides information for degree of inconsistency of an ontology (recall that inconsistency and incoherence are two different but related notions in DLs).…”

Section: Related Workmentioning

confidence: 99%

Measuring Incoherence in Description Logic-Based Ontologies

Hunter

2007

The Semantic Web

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Abstract. Ontologies play a core role in the success of the Semantic Web as they provide a shared vocabulary for different resources and applications. Developing an error-free ontology is a difficult task. A common kind of error for an ontology is logical contradiction or incoherence. In this paper, we propose some approaches to measuring incoherence in DLbased ontologies. These measures give an ontology engineer important information for maintaining and evaluating ontologies. We implement the proposed approaches using the KAON2 reasoner and provide some preliminary but encouraging empirical results.

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Stable cohesion metrics for evolving ontologies

et al. 2011

J. Softw. Maint. Evol.: Res. Pract.

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With the drastic development of semantic‐driven applications, assessing the quality of ontologies has received more attention. Measuring and assessing the quality of ontologies can help ontology engineers to control project management and reduce the risk of project failures. However, most of the existing ontology metrics for measuring and assessing the quality of ontologies are defined based on ontology structure, and neglect the stability of ontology measurement. In this paper, we concentrate on stable ontology measurement by using semantically derived ontology metrics. We propose four ontology cohesion metrics, which fully consider the implicitly expressed semantic information and are defined based on ontological semantics rather than ontology structure. Before measuring and assessing an ontology, we materialize a pre‐processing for stable ontology measurement by treating the ontology. The proposed ontology cohesion metrics are theoretically validated by the validation criteria of object‐oriented software. The experimental results show that we can successfully collect more semantic knowledge from the testing ontologies for stable ontology measurement by using the proposed ontology cohesion metrics. The ontology cohesion metrics proposed in this paper can be reasonably used as a cogent complementarity of existing ontology metrics. Copyright © 2010 John Wiley & Sons, Ltd.

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Measuring Inconsistency for Description Logics Based on Paraconsistent Semantics

Cited by 24 publications

References 15 publications

Dealing with Inconsistencies in DL-Lite Ontologies

Dealing with Inconsistencies in DL-Lite Ontologies

Measuring Incoherence in Description Logic-Based Ontologies

Stable cohesion metrics for evolving ontologies

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