Knowledge Graph Completion (KGC) has garnered massive research interest recently, and most existing methods are designed following a transductive setting where all entities are observed during training. Despite the great progress on the transductive KGC, these methods struggle to conduct reasoning on emerging KGs involving unseen entities. Thus, inductive KGC, which aims to deduce missing links among unseen entities, has become a new trend. Many existing studies transform inductive KGC as a graph classification problem by extracting enclosing subgraphs surrounding each candidate triple. Unfortunately, they still face certain challenges, such as the expensive time consumption caused by the repeat extraction of enclosing subgraphs, and the deficiency of entity-independent feature learning. To address these issues, we propose a global-local anchor representation (GLAR) learning method for inductive KGC. Unlike previous methods that utilize enclosing subgraphs, we extract a shared opening subgraph for all candidates and perform reasoning on it, enabling the model to perform reasoning more efficiently. Moreover, we design some transferable global and local anchors to learn rich entity-independent features for emerging entities. Finally, a global-local graph reasoning model is applied on the opening subgraph to rank all candidates. Extensive experiments show that our GLAR outperforms most existing state-of-the-art methods.

中文翻译：

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一子图万能：归纳知识图补全开放子图的高效推理

知识图补全（KGC）最近引起了广泛的研究兴趣，大多数现有方法都是按照传导设置设计的，在训练期间观察所有实体。尽管转导 KGC 取得了巨大进展，但这些方法很难对涉及不可见实体的新兴 KG 进行推理。因此，旨在推断不可见实体之间缺失链接的归纳式 KGC 已成为一种新趋势。许多现有研究通过提取每个候选三元组周围的封闭子图，将归纳 KGC 转化为图分类问题。不幸的是，他们仍然面临某些挑战，例如重复提取封闭子图导致的昂贵的时间消耗，以及与实体无关的特征学习的缺陷。为了解决这些问题，我们提出了一种用于归纳 KGC 的全局局部锚表示（GLAR）学习方法。与以前使用封闭子图的方法不同，我们为所有候选者提取一个共享的开放子图并对其进行推理，从而使模型能够更有效地进行推理。此外，我们设计了一些可转移的全局和局部锚点来学习新兴实体丰富的独立于实体的特征。最后，将全局局部图推理模型应用于开放子图以对所有候选者进行排名。大量实验表明，我们的 GLAR 优于大多数现有的最先进方法。