Incorporating Multi-Level Sampling with Adaptive Aggregation for Inductive Knowledge Graph Completion

Abstract

In recent years, Graph Neural Networks (GNNs) have achieved unprecedented success in handling graph-structured data, thereby driving the development of numerous GNN-oriented techniques for inductive knowledge graph completion (KGC). A key limitation of existing methods, however, is their dependence on pre-defined aggregation functions, which lack the adaptability to diverse data, resulting in suboptimal performance on established benchmarks. Another challenge arises from the exponential increase in irrelated entities as the reasoning path lengthens, introducing unwarranted noise and consequently diminishing the model’s generalization capabilities. To surmount these obstacles, we design an innovative framework that synergizes M ulti- L evel S ampling with an A daptive A ggregation mechanism (MLSAA). Distinctively, our model couples GNNs with enhanced set transformers, enabling dynamic selection of the most appropriate aggregation function tailored to specific datasets and tasks. This adaptability significantly boosts both the model’s flexibility and its expressive capacity. Additionally, we unveil a unique sampling strategy designed to selectively filter irrelevant entities, while retaining potentially beneficial targets throughout the reasoning process. We undertake an exhaustive evaluation of our novel inductive KGC method across three pivotal benchmark datasets and the experimental results corroborate the efficacy of MLSAA.