A Modularization-Based Approach to Finding All Justifications for OWL DL Entailments

Suntisrivaraporn, Boontawee; Qi, Guilin; Ji, Qiu; Haase, Peter

doi:10.1007/978-3-540-89704-0_1

Cited by 48 publications

(42 citation statements)

References 11 publications

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“…Minimal (mapping) repairs are typically referred to in the literature as mapping diagnoses [29] -a term coined by Reiter [36] and introduced to the field of ontology debugging in [39]. A repair or diagnosis can be computed by extracting the justifications for the unsatisfiable concepts (e.g., [38,22,43]), and selecting a hitting set of mappings to be removed, following a minimality criteria (e.g., the number of removed mappings). However, justification-based technologies do not scale when the number of unsatisfiabilities is large (a typical scenario in mapping repair problems [16]).…”

Section: Mapping Coherence and Mapping Repairmentioning

confidence: 99%

Detecting and Correcting Conservativity Principle Violations in Ontology-to-Ontology Mappings

Solimando

Jiménez-Ruiz

Guerrini

2014

Lecture Notes in Computer Science

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Abstract. In order to enable interoperability between ontology-based systems, ontology matching techniques have been proposed. However, when the generated mappings suffer from logical flaws, their usefulness may be diminished. In this paper we present an approximate method to detect and correct violations to the so-called conservativity principle where novel subsumption entailments between named concepts in one of the input ontologies are considered as unwanted. We show that this is indeed the case in our application domain based on the EU Optique project. Additionally, our extensive evaluation conducted with both the Optique use case and the data sets from the Ontology Alignment Evaluation Initiative (OAEI) suggests that our method is both useful and feasible in practice.

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Section: Mapping Coherence and Mapping Repairmentioning

confidence: 99%

Detecting and Correcting Conservativity Principle Violations in Ontology-to-Ontology Mappings

Solimando

Jiménez-Ruiz

Guerrini

2014

Lecture Notes in Computer Science

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“…[36,22,39,18]). However, justification-based technologies do not scale when the number of unsatisfiabilities is large (a typical scenario in mapping repair problems [16]).…”

Section: Definition 2 (Mapping Repairmentioning

confidence: 99%

Towards Annotating Potential Incoherences in BioPortal Mappings

Faria

Jiménez-Ruiz

Pesquita

et al. 2014

The Semantic Web – ISWC 2014

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Abstract. BioPortal is a repository for biomedical ontologies that also includes mappings between them from various sources. Considered as a whole, these mappings may cause logical errors, due to incompatibilities between the ontologies or even erroneous mappings. We have performed an automatic evaluation of BioPortal mappings between 19 ontology pairs using the mapping repair systems of LogMap and AgreementMakerLight. We found logical errors in 11 of these pairs, which on average involved 22% of the mappings between each pair. Furthermore, we conducted a manual evaluation of the repair results to identify the actual sources of error, verifying that erroneous mappings were behind over 60% of the repairs. Given the results of our analysis, we believe that annotating BioPortal mappings with information about their logical conflicts with other mappings would improve their usability for semantic web applications and facilitate the identification of erroneous mappings. In future work, we aim to collaborate with BioPortal developers in extending BioPortal with these annotations. Despite some community efforts to ensure a coordinated development of biomedical ontologies [38], many ontologies are being developed independently by different groups of experts and, as a result, they often cover the same or related subjects, but follow different modeling principles and use different entity naming schemes. Thus, to integrate data among applications, it is crucial to establish correspondences (called mappings) between the entities of the ontologies they use.In the last ten years, the semantic web and bioinformatics research communities have extensively investigated the problem of (semi-)automatically computing correspondences between independently developed ontologies, which is usually referred to as the ontology matching problem. Resulting from this effort are the growing number of ontology matching systems in development [8,7,37] and the large mapping repositories that have been created (e.g., [2,10]).

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“…One possibility to extract such a module is syntactic locality [6]. [30] showed that such locality-based modules contain all justifications with respect to an entailment and can provide orderof-magnitude performance improvements.…”

Section: Ontology Debuggingmentioning

confidence: 99%

ORE - A Tool for Repairing and Enriching Knowledge Bases

Lehmann

Bühmann

2010

Lecture Notes in Computer Science

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Abstract. While the number and size of Semantic Web knowledge bases increases, their maintenance and quality assurance are still difficult. In this article, we present ORE, a tool for repairing and enriching OWL ontologies. State-ofthe-art methods in ontology debugging and supervised machine learning form the basis of ORE and are adapted or extended so as to work well in practice. ORE supports the detection of a variety of ontology modelling problems and guides the user through the process of resolving them. Furthermore, the tool allows to extend an ontology through (semi-)automatic supervised learning. A wizard-like process helps the user to resolve potential issues after axioms are added.

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A Modularization-Based Approach to Finding All Justifications for OWL DL Entailments

Cited by 48 publications

References 11 publications

Detecting and Correcting Conservativity Principle Violations in Ontology-to-Ontology Mappings

Detecting and Correcting Conservativity Principle Violations in Ontology-to-Ontology Mappings

Towards Annotating Potential Incoherences in BioPortal Mappings

ORE - A Tool for Repairing and Enriching Knowledge Bases

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