Towards Linked Data Fact Validation through Measuring Consensus

author: Shuangyan Liu, Knowledge Media Institute (KMI), Open University (OU)
published: July 15, 2015,   recorded: June 2015,   views: 1434


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In the context of linked open data, different datasets can be interlinked together, thereby providing rich background knowledge for a dataset under examination. We believe that knowledge from interlinked datasets can be used to validate the accuracy of a linked data fact. In this paper, we present a novel approach for linked data fact validation using linked open data published on the web. This approach utilises owl:sameAs links for retrieving evidence triples, and a novel predicate similarity matching method. It computes the confidence score of an in- put fact based on weighted average of similarity of the evidence triples retrieved. We also demonstrate the feasibility of our approach using a sample of facts extracted from DBpedia.

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