A Meta-learning Framework for Enhancing Knowledge Reasoning Based on Contrastive Strategies
With the continuous emergence of new entities in the knowledge graph,it is difficult for existing embedded models to obtain new entity embeddings,resulting in a decrease in model prediction accuracy.Accordingly,the study proposes a meta-learning framework based on contrastive strategies to strengthen knowledge reasoning.First,the framework improves the extraction efficiency of implicit information by sorting the importance of paths,and then u-ses a comparison strategy to represent strongly associated paths.Excessive redundant information is removed according to the cosine similarity between negative paths and target relations,and finally meta-learning is used to transfer entity-inde-pendent embeddings.The experimental results show that in the link prediction knowledge map task,the value of the Hits@10 index can reach up to 96.24%,indicating that the framework can effectively improve prediction accuracy.
NETL
NSTL
万方数据
维普
