Relation Adaptive Representation Learning Based on Factual Information Interaction for One-Shot Knowledge Graph Completion

Few-shot, especially one-shot learning is a prominent research area in the field of knowledge graphs (KGs), aiming to utilize a limited number of triples with unseen relations as reference information for inferring missing knowledge. Recent research focuses on improving the semantic representation of entity pairs using interactions between their head and tail entities. However, this method only considers the reference information as the measurement criterion without taking into account the potential impact of it on the reasoning process of the model. In this paper, we propose a novel method that utilizes factual information interactions. Firstly, we learn static representations of entities based on their neighborhood information. Subsequently, we learn relation adaptive representations by incorporating the reference information. This interactive modeling strengthens the association between entity representations and task relations while suppressing irrelevant relations. Extensive experiments demonstrate that our model outperforms state-of-the-art methods on two public datasets. Remarkably, on the NELL-One dataset for one-shot link prediction, our model achieves an improvement of 11.8% in MRR compared to the best baseline model.