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Article type: Research Article
Authors: Wang, Chu; * | Wang, Jian
Affiliations: College of Electronic and Information Engineering, Tongji University, Shanghai, China
Correspondence: [*] Corresponding author. Chu Wang, College of Electronic and Information Engineering, Tongji University, Shanghai, China. E-mail: [email protected].
Abstract: The knowledge graph is widely used in industrial fields due to its structural characteristics. In order to reduce the cost of wrong decision-making, it is more important for the industrial knowledge graph to guarantee the quality and comprehensiveness of knowledge. In order to obtain the trustworthiness information of triples in the graph, this paper proposed a model to evaluate triple trustworthiness for industrial knowledge graphs(TT-IKG) and obtains the final score of triples by fusing the output of three sub-modules that investigate the local confidence of triples, the confidence of schema-matching and the confidence of global paths. Experiments on the real industrial knowledge graph of enterprises verified the effectiveness of the model, and the experimental results on the error detection and graph completion tasks of the knowledge graph show that the effect is better than that of other models.
Keywords: Knowledge graph, triple trustworthiness, industrial domain, representation learning
DOI: 10.3233/JIFS-231449
Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 2967-2977, 2023
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