Knowledge Graph Embedding Based on Relevance and Inner Sequence of Relations

Knowledge graph Embedding can obtain the low-dimensional dense vectors, which helps to reduce the high dimension and heterogeneity of Knowledge graph (KG), and enhance the application of KG. Many existing methods focus on building complex models, elaborate feature engineering or increasing learning parameters, to improve the performance of embedding. However, these methods rarely capture the influence of intrinsic relevance and inner sequence of the relations in KG simultaneously, while balancing the number of parameters and the complexity of the algorithm. In this paper, we propose a concatenate knowledge graph embedding method based on relevance and inner sequence of relations (KGERSR). In this model, for each \(<head, relation, tail>\) triple, we use two partially shared gates for head and tail entities. Then we concatenate these two gates to capture the inner sequence information of the triples. We demonstrate the effectiveness of the proposed KGERSR on standard FB15k-237 and WN18RR datasets, and it gives about 2% relative improvement over the state-of-the-art method in terms of Hits@1, and Hits@10. Furthermore, KGERSR has fewer parameters than ConmplEX and TransGate. These results indicate that our method could be able to find a better trade-off between complexity and performance.