Triple trustworthiness evaluation for knowledge graph of industrial domain

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.