Mixture of Length and Pruning Experts for Knowledge Graphs Reasoning

TL;DR

This paper introduces MoKGR, a novel mixture-of-experts framework for knowledge graph reasoning that personalizes path exploration by adapting reasoning depth and pruning paths based on query complexity and informativeness.

Contribution

MoKGR is the first to combine length and pruning experts for personalized, adaptive reasoning path selection in knowledge graph reasoning tasks.

Findings

MoKGR outperforms existing models on multiple benchmarks.

Personalized path exploration improves reasoning accuracy.

Framework is effective in both transductive and inductive settings.

Abstract

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.