Enhanced faceted browsing of a WW1 dataset through ontology alignment

Crotti, Ademar; Walshe, Brian; O’Sullivan, Declan

doi:10.1145/2837185.2837259

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…• In addition to the use case of matching, the application of the approach presented is not only confined to LOD interlinking. For example, it could potentially be used to support: refinement of ontologies (Crotti, Walshe & O'Sullivan, 2015); to improve quality through contributing to type prediction ; ontology learning and refinement (Lehmann, Auer, Buhmann & Tramp, 2011;Volker & Niepert, 2011); query rewriting (Gillet, Trojahn, Haemmerle & Pradel, 2013).…”

Section: Introductionmentioning

confidence: 99%

Bayes-ReCCE

Walshe

Brennan

O’Sullivan

2016

International Journal on Semantic Web and Information Systems

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Linked Open Data consists of a large set of structured data knowledge bases which have been linked together, typically using equivalence statements. These equivalences usually take the form of owl:sameAs statements linking individuals, but links between classes are far less common. Often, the lack of linking between classes is because the relationships cannot be described as elementary one to one equivalences. Instead, complex correspondences referencing multiple entities in logical combinations are often necessary if we want to describe how the classes in one ontology are related to classes in a second ontology. In this paper the authors introduce a novel Bayesian Restriction Class Correspondence Estimation (Bayes-ReCCE) algorithm, an extensional approach to detecting complex correspondences between classes. Bayes-ReCCE operates by analysing features of matched individuals in the knowledge bases, and uses Bayesian inference to search for complex correspondences between the classes these individuals belong to. Bayes-ReCCE is designed to be capable of providing meaningful results even when only small amounts of matched instances are available. They demonstrate this capability empirically, showing that the complex correspondences generated by Bayes-ReCCE have a median F1 score of over 0.75 when compared against a gold standard set of complex correspondences between Linked Open Data knowledge bases covering the geographical and cinema domains. In addition, the authors discuss how metadata produced by Bayes-ReCCE can be included in the correspondences to encourage reuse by allowing users to make more informed decisions on the meaning of the relationship described in the correspondences.

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Section: Introductionmentioning

confidence: 99%

Bayes-ReCCE

Walshe

Brennan

O’Sullivan

2016

International Journal on Semantic Web and Information Systems

Self Cite

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Detecting Restriction Class Correspondences in Linked Data

Walshe

Brennan

O’Sullivan

2018

Innovations, Developments, and Applications of Semantic Web and Information Systems

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Linked Data consists of many structured data knowledge bases that have been interlinked, often using equivalence statements. These equivalences usually take the form of owl:sameAs statements linking individuals, links between classes are far less common Often, the lack of class links is because their relationships cannot be described as one to one equivalences. Instead, complex correspondences referencing logical combinations of multiple entities are often needed to describe how the classes in an ontology are related to classes in a second ontology. This chapter introduces a novel Bayesian Restriction Class Correspondence Estimation (Bayes-ReCCE) algorithm, an extensional approach to detecting complex correspondences between classes. Bayes-ReCCE operates by analysing features of matched individuals in the knowledge bases, and uses Bayesian inference to search for complex correspondences between the classes these individuals belong to. Bayes-ReCCE is designed to be capable of providing meaningful results even when only small numbers of matched instances are available.

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Enhanced faceted browsing of a WW1 dataset through ontology alignment

Cited by 2 publications

References 4 publications

Bayes-ReCCE

Bayes-ReCCE

Detecting Restriction Class Correspondences in Linked Data

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