关键词: 知识图谱, 图神经网络, 关系预测, DistMult解码器

Abstract: State Grid Gansu Electric Power Academy hopes to construct a knowledge graph of the power industry through a large amount of scientific research literature and deeply explore the potential correlations in the knowledge graph.The relationship prediction model is a key technology for solving such problems and an important technology in knowledge graphs,which has been a research hotspot for researchers in recent years.A large number of papers and experiments have demonstrated that the framework combining encoder and decoder performs well in relation prediction tasks.Under this framework,due to the advancement of graph neural network technology,there have been many works in recent years that have improved the performance of relationship prediction by using graph neural networks as encoders and optimizing them,while neglecting the role of decoders.Taking inspiration from cosine similarity,this paper proposes a novel decoder COS DistMult based on DistMult and conducts comparative experiments on real datasets,and the experimental results indicate that the evaluation indicator Hits@10of the relationship prediction model increases by 2%.It is proves that optimizing the decoder structure is an effective method in relation prediction tasks based on an encoder decoder framework.

Key words: Knowledge graph, Graph neural network, Relation prediction, DistMult decoder