Towards Linked Data Fact Validation through Measuring Consensus.
Shuangyan Liu, Mathieu d’Aquin, Enrico Motta · 2015
Abstract. 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.