基于关系学习的小样本知识图谱补全模型OA北大核心CSTPCD

In few-show knowledge graphs,the representation of relationships between entity pairs was diverse and complex.However,existing few-show knowledge graph completion methods commonly suffered from insufficient re-lational learning capabilities and the neglect of contextual semantics associated with entities.To address these chal-lenges,a novel approach called the few-shot relation learning completion model(FRLC)was proposed.Firstly,during the process of aggregating high-order neighborhood entity information,a gating mechanism was introduced to mitigate the adverse effects of noise on neighbors while enriching the representation of central entities.Secondly,in the phase of relation representation learning,the correlations among entity pairs in a reference set were leveraged to obtain more accurate relationship representations.Lastly,within the Transformer-based learning framework,an LSTM structure was incorporated to further capture contextual semantic information of entities and relationships,which was used for predicting new factual knowledge.To validate the effectiveness of FRLC,comparative experi-ments were conducted on the publicly available NELL-One and Wiki-One datasets,in which FRLC was compared with six few-shot knowledge graph completion models and five traditional models for 5-shot link prediction.The ex-perimental results showed improvements in FRLC across four metrics:MRR,Hits@10,Hits@5,and Hits@1,demonstrating the model's effectiveness.