Knowledge graph embedding model based on multi-scale convolution of capsule network
This paper is based on the excellent dimensional information mining ability of capsule neural networks,and incorporates multi-scale convolution to further enhance their feature extraction and interac-tion capabilities.A capsule network knowledge graph embedding model based on multi-scale convolution is proposed.Firstly,the initialization embedding vectors for entities and relationships are trained using the TransE algorithm.Secondly,different feature maps are generated through multi-scale convolution,and the resulting feature maps are fused to form corresponding capsules.Finally,dynamic routing is used to speci-fy the connection from the first layer capsule to the second layer capsule.The second layer capsule obtained through routing is then used to obtain the final vector length using the squash function,which determines the confidence level of the triplet.Compared with the embedding models CapsE,the proposed model in this paper has a 1.8％improvement in Hit@10 and 1.4％improvement in MRR metrics on the WN18RR dataset,and a 2.2％improvement in Hit@10 and 4.8％improvement in MR on the FB15k-237 dataset.
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