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 Cumulative Path-Level Semantic Reasoning for inductive knowledge graph completion (CPSR) framework, which simultaneously captures both the structural and semantic information of KGs to enhance the inductive KGC task. Specifically, the proposed CPSR employs a query-dependent masking module to adaptively mask noisy structural information while retaining important information closely related to the targets. Additionally, CPSR 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 CPSR 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，但在涉及新兴实体的场景中往往难以有效工作。归纳式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。尽管现有的归纳式知识图谱补全方法已取得一定成功，它们仍面临一些挑战，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，本文提出了一种用于归纳知识图谱补全的累积路径级语义推理框架，该框架同时捕捉知识图谱的结构信息和语义信息，以增强归纳式知识图谱补全任务。具体而言，所提出的CPSR框架采用查询依赖的掩码模块，自适应地屏蔽噪声结构信息，同时保留与目标密切相关的关键信息。此外，CPSR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协同影响。实验结果表明，CPSR实现了最先进的性能。