Few-Shot Knowledge Graph Completion Based on Selective Attention

Abstract

Most few-shot knowledge graph completion models have some problems, such as low ability to learn relation representation and rarely attaching importance to the relative location and interaction between query entity pair when the relation between entities is complex or triples’ neighborhood is sparse. A selective attention mechanism and interaction awareness (SAIA) based few-shot knowledge graph completion algorithm is proposed. Firstly, by introducing selective attention mechanism in the process of aggregating neighbor information, the neighbor encoder pays more attention to important neighbors to reduce adverse effects of noise neighbors. Secondly, SAIA utilizes the information related to task relation in the background knowledge graph to learn more accurate relation embedding in the process of relationship representation learning. Finally, in order to mine the interaction information and location information between entities in knowledge graph, a common interaction rate index (CIR) of entity pair is designed to measure the degree of association between entities in 3-hop path. Then, SAIA combines entity pair semantic information to predict new fact. Experimental results show that SAIA outperforms the state-of-the-art few-shot knowledge graph completion methods. Compared with the optimal results of baseline models, the proposed method achieves 5-shot link prediction performance improvement of 0.038, 0.011, 0.028 and 0.052 on NELL-one dataset and 0.034，0.037，0.029 and 0.027 on Wiki-one dataset by the metric MRR, Hits@10, Hits@5 as well as Hits@1, which verifies the effectiveness and feasibility of SAIA.

Keywords

• knowledge graph; knowledge graph completion; representation learning; few-shot relation; attention mechanism