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.

基于全局和局部锚点表示（GLAR）学习的归纳式知识图补全方法，在开放子图上进行推理，以更高效地评估候选实体，并通过全局和局部锚点学习丰富的独立特征来解决传统方法的问题。实验证明，GLAR 优于现有大多数最先进的方法。