让知识图谱推理自动选路径长度和筛选路径，更智能。
Mixture of Length and Pruning Experts for Knowledge Graphs Reasoning
- 用两个专家模块分别动态选路径长度和筛选信息量高的路径
- 在多个基准数据集上表现优于现有方法，尤其在复杂查询下优势明显
- 适合需要精准推理的问答、推荐系统等场景
知识图谱推理旨在从结构化知识库中推断新事实，在自然语言处理中至关重要。其效果依赖于构建有信息量且语境相关的推理路径。然而，现有图神经网络通常采用固定、不随查询变化的路径探索策略，难以适应多样的语言语境和语义差异。为此，我们提出MoKGR——一种混合专家框架，通过两个互补组件实现个性化路径探索：(1) 路径长度混合专家，根据查询复杂度自适应选择并加权候选路径长度，提供查询特定的推理深度；(2) 路径剪枝混合专家，从互补视角评估候选路径，保留每条查询中最信息丰富的路径。在多个基准数据集上的全面实验表明，MoKGR在归纳和直推设置下均表现更优，验证了个性化路径探索在知识图谱推理中的有效性。
原文摘要 · Abstract (English)
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.
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