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Detecting Restriction Class Correspondences in Linked Data: The Bayes-ReCCE Bayesian Model Approach
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Author(s): Brian Walshe (Barclays, UK), Rob Brennan (Trinity College Dublin, Ireland)and Declan O'Sullivan (Trinity College Dublin, Ireland)
Copyright: 2018
Pages: 31
Source title:
Innovations, Developments, and Applications of Semantic Web and Information Systems
Source Author(s)/Editor(s): Miltiadis D. Lytras (American College of Greece, Greece), Naif Aljohani (King Abdulaziz University, Saudi Arabia), Ernesto Damiani (University of Milan, Italy)and Kwok Tai Chui (The Open University of Hong Kong, Hong Kong)
DOI: 10.4018/978-1-5225-5042-6.ch008
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Abstract
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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