BEFSR: A Multiple Attention-Based Model Considering Bidirectional Entity Information Flows and Few-Shot Relations

Xiaobo Guo, Neng Gao, Fali Wang, Nan Mu, Lei Wang, Yao Dong

Abstract: The traditional knowledge representation learning (KRL) models treat each triplet in a knowledge base independently, so they can not make full use of the neighborhood information across triplets. KRL models based on graph attention networks (GAT) can not only capture feature interactions across triplets, but also further distinguish the importance of neighbor entities. Recently, we find that there are two flaws in GAT-based KRL models: (1) Ignoring the bidirectionality of information flows leads to insufficient utilization of entity neighborhood information. When encapsulating the neighborhood information, only the forward information flows flowing into the target entity are considered, but the backward information flows flowing out are neglected. (2) The unified update process for all relations causes the useful information related the few-shot relations to be diluted. We propose a multiple attention-based model considering bidirectional entity information flows and few-shot relations (BEFSR). In our model, a GAT-based attention framework is used to integrate forward information flows and backward information flows of each triplet respectively to capture feature interactions across triplets, and an LSTM-based attention framework is adopted to gradually aggregate the entity-pair information for few-shot relations’ updating. In BEFSR, entities and relations can be updated more appropriately. Experiments demonstrate that BEFSR outperforms state-of-the-art KRL models in knowledge base completion task.