Mixture of Length and Pruning Experts for Knowledge Graphs Reasoning
This addresses the limitation of existing graph neural networks in adapting to diverse linguistic contexts and semantic nuances for knowledge graph reasoning, representing an incremental improvement.
The paper tackled the problem of rigid, query-agnostic path exploration in knowledge graph reasoning by proposing MoKGR, a mixture-of-experts framework that personalizes path exploration with length and pruning experts, resulting in superior performance on diverse benchmarks in transductive and inductive settings.
Knowledge Graph (KG) reasoning, which aims to infer new facts from structured knowledge repositories, plays a vital role in Natural Language Processing (NLP) systems. Its effectiveness critically depends on constructing informative and contextually relevant reasoning paths. However, existing graph neural networks (GNNs) often adopt rigid, query-agnostic path-exploration strategies, limiting their ability to adapt to diverse linguistic contexts and semantic nuances. To address these limitations, we propose \textbf{MoKGR}, a mixture-of-experts framework that personalizes path exploration through two complementary components: (1) a mixture of length experts that adaptively selects and weights candidate path lengths according to query complexity, providing query-specific reasoning depth; and (2) a mixture of pruning experts that evaluates candidate paths from a complementary perspective, retaining the most informative paths for each query. Through comprehensive experiments on diverse benchmark, MoKGR demonstrates superior performance in both transductive and inductive settings, validating the effectiveness of personalized path exploration in KGs reasoning.