Aquatic Medicine Knowledge Graph Completion Based on Hybrid Convolution

Abstract

Aquatic medicine knowledge graph is an effective means to realize intelligent aquaculture. Graph completion technology is key to improving the quality of knowledge graph construction. However, the difficulty of semantic discrimination among similar entities and inconspicuous semantic features result in low accuracy when completing aquatic medicine knowledge graph with complex relationships. In this study, an aquatic medicine knowledge graph completion method (TransH+HConvAM) is proposed. Firstly, TransH is applied to split the vector plane between entities and relations, ameliorating the poor completion effect caused by low semantic resolution of entities. Then, hybrid convolution is introduced to obtain the global interaction of triples based on the complete interaction between head/tail entities and relations, which improves the semantic features of triples and enhances the completion effect of complex relationships in the graph. Experiments are conducted to verify the performance of the proposed method. The MR, MRR and Hit@10 of the TransH+HConvAM are found to be 674, 0.339, and 0.361, respectively. This study shows that the model effectively overcomes the poor completion effect of complex relationships and improves the construction quality of the aquatic medicine knowledge graph, providing technical support for intelligent aquaculture.

Keywords

aquatic medicine knowledge graph / graph completion / hybrid convolution / global features