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Measuring Accuracy of Triples in Knowledge Graphs

Authors: 

Shuangyan Liu, Mathieu d'Aquin, Enrico Motta

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
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.
Conference Name: 
LDK 2017: Language, Data, and Knowledge
Proceedings: 
International Conference on Language, Data and Knowledge, LDK 2017
Digital Object Identifer (DOI): 
10.1007/978-3-319-59888-8_29
Publication Date: 
27/05/2017
Conference Location: 
Ireland
Research Group: 
Institution: 
National University of Ireland, Galway (NUIG)
Open access repository: 
No
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