Abstract
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.
Abstract (translated)
知识图谱完成（KGC）最近吸引了大量研究兴趣，而且大多数现有方法都是在训练过程中采用转换设置的，其中所有实体都在观察过程中。尽管在转换式KGC方面取得了很大进展，但这些方法在处理涉及未见实体的 emergence KG 时仍然存在挑战。因此，归纳式KGC，旨在从未见实体中推断缺失链接，已成为一个新的趋势。许多现有研究将归纳式KGC转换为一个图分类问题，通过提取围绕每个候选三元组的内包容子图来完成。然而，他们仍然面临着某些挑战，例如由于重复提取内包容子图而产生的高时间消耗，以及实体独立特征学习不足的问题。为了解决这些问题，我们提出了一个全局-局部锚表示（GLAR）学习方法来解决归纳式KGC。与之前的方法不同，我们为所有候选者提取共享的内包容子图，并在其上进行推理，使模型能够更有效地进行推理。此外，我们还设计了一些可转移的全局和局部锚来学习新兴实体的丰富实体独立特征。最后，在打开子图上应用全局-局部图推理模型对所有候选者进行排名。大量实验证明，我们的GLAR超越了大多数现有最先进的方法。
URL
https://arxiv.org/abs/2404.15807
