Meta-learning framework with updating information flow for enhancing inductive prediction

Inductive relation prediction, which handles unseen entities at the reasoning stage, has the potential to complete continuously expanding knowledge graphs. Existing inductive approaches predominantly focus on providing alternatives predicted by the subgraph structure of the target. However, many approaches neglect the correlation between semantic relationships, which does not mine additional implicit information. In this paper, we propose MUIF, a meta-learning framework with an updated information flow that addresses these challenges. Further, MUIF aims to enhance the role of relational semantics in inductive prediction, while emphasizing the importance of distinguishing relational paths. We propose an updated information flow mechanism for strengthening the relational pass, and we use a contrastive strategy to represent strongly associated paths. Further, meta-learning is applied to transfer the updated total embedding, thereby enhancing prediction accuracy. Further, the Hits@10 of MUIF can reach up to 97.03% in the link prediction experiments, thereby demonstrating the excellent performance of the proposed approach.