NAEP: Neighborhood Multihead Attention with Evidential Conditional Neural Processes for Few-Shot Knowledge Graph Completion*

In few-shot knowledge graph completion (FKGC), inferring new relationships from limited data is a key challenge. Current FKGC frameworks often struggle to detect subtle differences among entity types and handle noise in sparse environments, impacting their performance. Aligning relational representations with entity and relationship data, while managing prediction uncertainties, is another significant hurdle. We propose the NAEP model, which integrates a hierarchical relational gating graph attention network to discern complex relational dynamics and differentiate entity types. It employs neighborhood multihead attention for path encoding to reduce noise and incorporates an Evidential Conditional Neural Processes encoder to handle predictive uncertainties. Preliminary results show that NAEP performs robustly across various datasets, from large ones like NELL and WIKI to smaller ones like FB15K-237, demonstrating its versatility and superior performance.