Over the years, reasoning over knowledge graphs (KGs), which aims to infer
new conclusions from known facts, has mostly focused on static KGs. The
unceasing growth of knowledge in real life raises the necessity to enable the
inductive reasoning ability on expanding KGs. Existing inductive work assumes
that new entities all emerge once in a batch, which oversimplifies the real
scenario that new entities continually appear. This study dives into a more
realistic and challenging setting where new entities emerge in multiple
batches. We propose a walk-based inductive reasoning model to tackle the new
setting. Specifically, a graph convolutional network with adaptive relation
aggregation is designed to encode and update entities using their neighboring
relations. To capture the varying neighbor importance, we employ a query-aware
feedback attention mechanism during the aggregation. Furthermore, to alleviate
the sparse link problem of new entities, we propose a link augmentation
strategy to add trustworthy facts into KGs. We construct three new datasets for
simulating this multi-batch emergence scenario. The experimental results show
that our proposed model outperforms state-of-the-art embedding-based,
walk-based and rule-based models on inductive KG reasoning.

本研究提出了一种基于图卷积网络和关系聚合的归纳推理模型，利用自适应关系聚合和反馈注意机制对实体进行编码和更新，并针对新实体的稀疏性问题提出了链接增强策略。实验结果表明该模型在归纳知识图谱推理方面优于现有的基于嵌入、基于行走和基于规则的模型。