BHRAM: a knowledge graph embedding model based on bidirectional and heterogeneous relational attention mechanism

Knowledge graph embedding (KGE) is a method designed to predict missing relations between entities in a knowledge graph (KG), which has garnered much attention in recent years due to the incompleteness of KGs. However, existing KGE models have limitations in dealing with heterogeneous KGs and relation direction prediction. To address this issue, a novel KGE model called BHRAM is proposed. It is based on a bidirectional and heterogeneous relational attention mechanism. Specifically, BHRAM comprises three primary components, namely entity aggregation, relation aggregation and triplet prediction. The entity aggregation module divides the adjacency matrix into original and reverse relation adjacency matrices, using graph convolution to aggregate node features and subsequently form entity embedding representations. The relation aggregation module leverages bidirectional relations for feature extraction, learns the weight information of diverse paths independently and generates embedding representations of relation paths through an aggregation function. Finally, the triplet prediction module utilizes a score function for probabilistic predictions. To validate the superiority of BHRAM, comprehensive experiments were conducted on four well-known datasets, including baseline comparisons, relation classification tasks and ablation study. The results demonstrate that BHRAM significantly outperforms the other baselines on the FB15k-237, Kinship and UMLS datasets, while achieving similar or better performance than the baselines on the WN18RR dataset. These findings indicate that BHRAM can serve as a robust and effective model for addressing the heterogeneity in KGs.