发表机构
School of Mathematics, Shandong University; School of Physical and Mathematical Sciences, Nanyang Technological University; Data Science Institute, Shandong University（山东大学数学学院; 南洋理工大学物理与数学科学学院; 山东大学数据科学研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PEARL是一种结合上下文子图建模、LLM引导路径检索与双视图对比学习的归纳式知识图谱补全框架，在WN18RR等三个基准数据集上取得最优平均Hits@10，验证了其核心组件的有效性。
AI 中文摘要
归纳式知识图谱补全（IKGC）旨在预测训练过程中未出现的实体间的缺失链接，要求模型学习可迁移的关系与结构模式。现有基于子图和路径的方法通常对关系路径进行编码时，不考虑其周围的查询子图，而路径的预测相关性会随结构上下文变化。本文提出PEARL，即路径-实体对齐关系学习框架，该框架将路径建模为上下文条件推理信号。PEARL从查询实体的邻域并集中构建查询特定的上下文子图，并使用大语言模型（LLM）引导的检索器提炼语义相关的路径；随后在路径、上下文实体和全局子图表示上构建二分交互图，使路径嵌入能适配局部与全局结构证据。为抑制扩大上下文引入的噪声，PEARL采用双视图对比目标，以提升随机上下文扰动下的表示一致性。在WN18RR、FB15k-237和NELL-995上的实验表明，PEARL在这三个基准数据集上的对比IKGC方法中，获得了最佳的平均Hits@10指标；消融研究、效率分析和案例研究进一步验证了上下文子图建模、语义路径检索、路径-实体交互及对比正则化的贡献。
英文摘要
Inductive knowledge graph completion (IKGC) aims to predict missing links involving entities unseen during training, requiring models to learn transferable relational and structural patterns. Existing subgraph- and path-based approaches often encode relational paths independently of their surrounding query subgraphs, although the predictive relevance of a path may vary across structural contexts. We propose PEARL, a Path-Entity Aligned Relational Learning framework that models paths as context-conditioned reasoning signals. PEARL constructs a query-specific contextual subgraph from the union of the query entities' neighborhoods and uses a large language model (LLM)-guided retriever to distill semantically relevant paths. It then builds a bipartite interaction graph over paths, contextual entities, and a global subgraph representation, allowing path embeddings to adapt to local and global structural evidence. To suppress noise introduced by the enlarged context, PEARL employs a dual-view contrastive objective that promotes representation consistency under stochastic contextual perturbations. Experiments on WN18RR, FB15k-237, and NELL-995 show that PEARL obtains the best average Hits@10 among the compared IKGC methods on all three benchmarks. Ablation studies, efficiency analyses, and case studies further validate the contributions of contextual subgraph modeling, semantic path retrieval, path-entity interaction, and contrastive regularization.
