Inductive Knowledge Graph Completion (KGC) aims to infer missing facts between newly emerged entities within knowledge graphs (KGs), posing a significant challenge. While recent studies have shown promising results in inferring such entities through knowledge subgraph reasoning, they suffer from (i) the semantic inconsistencies of similar relations, and (ii) noisy interactions inherent in KGs due to the presence of unconvincing knowledge for emerging entities. To address these challenges, we propose a Semantic Structure-aware Denoising Network (S$^2$DN) for inductive KGC. Our goal is to learn adaptable general semantics and reliable structures to distill consistent semantic knowledge while preserving reliable interactions within KGs. Specifically, we introduce a semantic smoothing module over the enclosing subgraphs to retain the universal semantic knowledge of relations. We incorporate a structure refining module to filter out unreliable interactions and offer additional knowledge, retaining robust structure surrounding target links. Extensive experiments conducted on three benchmark KGs demonstrate that S$^2$DN surpasses the performance of state-of-the-art models. These results demonstrate the effectiveness of S$^2$DN in preserving semantic consistency and enhancing the robustness of filtering out unreliable interactions in contaminated KGs.

翻译：归纳知识图谱补全旨在推断知识图谱中新出现实体间缺失的事实，这是一项重大挑战。尽管近期研究通过知识子图推理在推断此类实体方面取得了有前景的结果，但它们仍存在以下问题：(i) 相似关系的语义不一致性，以及(ii) 由于新出现实体存在不可信知识而导致知识图谱固有的噪声交互。为应对这些挑战，我们提出了一种用于归纳知识图谱补全的语义结构感知去噪网络。我们的目标是学习适应性强的通用语义和可靠结构，以提炼一致的语义知识，同时保留知识图谱内的可靠交互。具体而言，我们在封闭子图上引入了一个语义平滑模块，以保留关系的通用语义知识。我们结合了一个结构精炼模块来过滤不可靠的交互并提供额外知识，从而保留目标链接周围鲁棒的结构。在三个基准知识图谱上进行的大量实验表明，S$^2$DN的性能超越了现有最先进模型。这些结果证明了S$^2$DN在保持语义一致性和增强过滤受污染知识图谱中不可靠交互的鲁棒性方面的有效性。