Measuring Accuracy of Triples in Knowledge Graphs

Liu, Shuangyan; d'Aquin, Mathieu and Motta, Enrico (2017). Measuring Accuracy of Triples in Knowledge Graphs. In: Language, Data, and Knowledge: First International Conference, LDK 2017 Galway, Ireland, June 19–20, 2017 Proceedings (Gracia, Jorge; Bond, Francis; McCrae, John P.; Buitelaar, Paul; Chiarcos, Christian and Hellmann, Sebastian eds.), Lecture Notes in Computer Science, Springer, Cham, pp. 343–357.



An increasing amount of large-scale knowledge graphs have been constructed in recent years. Those graphs are often created from text-based extraction, which could be very noisy. So far, cleaning knowledge graphs are often carried out by human experts and thus very inefficient. It is necessary to explore automatic methods for identifying and eliminating erroneous information. In order to achieve this, previous approaches primarily rely on internal information i.e. the knowledge graph itself. In this paper, we introduce an automatic approach, Triples Accuracy Assessment (TAA), for validating RDF triples (source triples) in a knowledge graph by finding consensus of matched triples (among target triples) from other knowledge graphs. TAA uses knowledge graph interlinks to find identical resources and apply different matching methods between the predicates of source triples and target triples. Then based on the matched triples, TAA calculates a confidence score to indicate the correctness of a source triple. In addition, we present an evaluation of our approach using the FactBench dataset for fact validation. Our findings show promising results for distinguishing between correct and wrong triples.

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