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Trustworthiness Measurement for Multimedia Domain Knowledge Graph.

IEEE international Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)(2022)

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Abstract
With the upsurge of knowledge graph research, the multimedia field supports the upper multimedia services by constructing the domain knowledge graph to improve the user experience quality of multimedia services. However, the quality of knowledge graph will have a great impact on the performance of the upper multimedia service supported by it. The existing quantitative methods of knowledge graph quality have some problems, such as incomplete information utilization and so on. Therefore, this paper proposes a trustworthiness measurement method of joint entity type embedding and transformer encoder, which is quantified respectively from the local level and the global level, makes full use of the rich semantic information in the knowledge graph, and comprehensively evaluates the quality of the knowledge graph. The experimental results show that the method proposed in this paper can solve the problem that the traditional methods can not detect the matching error of entity and entity type under the condition that the error detection effect of traditional triples is basically unchanged.
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Key words
knowledge graph, representation learning, transFormer encoder, trustworthiness
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