Multihop neighbor aggregator and contrast learning for few-shot knowledge graph complete

Knowledge graph (KG) is evolving rapidly and play an important role in many applications. Recently, the few-shot knowledge graph completion (FKGC) task, which involves predicting missing information based on a limited number of known facts, has garnered growing interest from both practitioners and researchers. However, these methods often fail to fully utilize neighboring information and overlook the semantic distance between similar entities. To address these problems, we introduce a multi-hop neighbor aggregator based on CNN which designs to make comprehensive use of neighbor information. Additionally, we employ contrastive learning to reduce the semantic distance between similar entities. Compared to the leading baseline GANA, our model shows an improvement of 0.4% and 0.4% on NELL in terms of Hits@1 and Hits@5, respectively, and 5%, 1.4%, and 0.4% on Wiki in terms of Hits@1, Hits@5, and Hits@10. Extensive experiments demonstrate that our method performs exceptionally well on two public datasets.