Multisource hierarchical neural network for knowledge graph embedding

Abstract

Link prediction for knowledge graphs aims to obtain missing nodes in triples. In recent years, link prediction methods have made specific achievements in knowledge graph embedding. However, knowledge graphs are characteristic of the heterogeneity of multiple types of entities and relations. A vital issue is efficiently extracting complex graph information and constructing a knowledge-semantic fusion of multiple features. To overcome these issues, a novel link prediction framework based on a multisource hierarchical neural network for knowledge graph embedding (MSHE) is proposed. In particular, mapping functions obtain entities and relations from low- to high-dimensional mapping sources. The combination of mapping sources and entity-relation sources constitutes multisource knowledge information, which facilitates the integration of complex heterogeneous entities and relations. Unlike training a single independent network, the hierarchical embedding network proposed in this paper accumulates feature information at multiple levels. Then, to fuse feature information, our Highway multilayer perceptron (MLP) inductively generates high-quality knowledge information. Through extensive experiments, MSHE’s knowledge graph embedding outperformed the state-of-the-art baselines on FB15k-237 and YAGO3-10. Furthermore, MSHE achieves a Hits@10 score that is 3.8% and 2.7% higher than that of ComplexGCN on FB15K-237 and WN18RR, respectively. MSHE achieves a higher score in Hits@1 than DCN 10.0% in the dataset YAGO3-10. The experiments show that the MSHE achieved excellent results in the four datasets of comparative experiments.