Conventional Knowledge graph completion (KGC) methods aim to infer missing information in incomplete Knowledge Graphs (KGs) by leveraging existing information, which struggle to perform effectively in scenarios involving emerging entities. Inductive KGC methods can handle the emerging entities and relations in KGs, offering greater dynamic adaptability. While existing inductive KGC methods have achieved some success, they also face challenges, such as susceptibility to noisy structural information during reasoning and difficulty in capturing long-range dependencies in reasoning paths. To address these challenges, this paper proposes the Query-Enhanced Adaptive Semantic Path Reasoning (QASPR) framework, which simultaneously captures both the structural and semantic information of KGs to enhance the inductive KGC task. Specifically, the proposed QASPR employs a query-dependent masking module to adaptively mask noisy structural information while retaining important information closely related to the targets. Additionally, QASPR introduces a global semantic scoring module that evaluates both the individual contributions and the collective impact of nodes along the reasoning path within KGs. The experimental results demonstrate that QASPR achieves state-of-the-art performance.

翻译：传统的知识图谱补全方法旨在利用知识图谱中已有的信息来推断不完整知识图谱中缺失的信息，这些方法在处理涉及新兴实体的场景时往往效果不佳。归纳知识图谱补全方法能够处理知识图谱中出现的新实体和新关系，具有更强的动态适应性。尽管现有的归纳知识图谱补全方法已取得一定成功，但它们也面临一些挑战，例如在推理过程中易受噪声结构信息干扰，以及难以捕捉推理路径中的长程依赖关系。为应对这些挑战，本文提出了查询增强的自适应语义路径推理框架，该框架同时捕获知识图谱的结构信息和语义信息，以增强归纳知识图谱补全任务。具体而言，所提出的QASPR框架采用一个查询相关的掩码模块，自适应地屏蔽噪声结构信息，同时保留与目标紧密相关的重要信息。此外，QASPR引入了一个全局语义评分模块，用于评估知识图谱中推理路径上各个节点的独立贡献及其集体影响。实验结果表明，QASPR实现了最先进的性能。