Knowledge representation learning has been commonly adopted to incorporate
knowledge graph (KG) into various online services. Although existing knowledge
representation learning methods have achieved considerable performance
improvement, they ignore high-order structure and abundant attribute
information, resulting unsatisfactory performance on semantics-rich KGs.
Moreover, they fail to make prediction in an inductive manner and cannot scale
to large industrial graphs. To address these issues, we develop a novel
framework called KGNN to take full advantage of knowledge data for
representation learning in the distributed learning system. KGNN is equipped
with GNN based encoder and knowledge aware decoder, which aim to jointly
explore high-order structure and attribute information together in a
fine-grained fashion and preserve the relation patterns in KGs, respectively.
Extensive experiments on three datasets for link prediction and triplet
classification task demonstrate the effectiveness and scalability of KGNN
framework.

KGNN 框架采用 GNN 编码器和知识感知解码器，旨在以细粒度的方式共同探索高阶结构和属性信息，同时在知识图谱中保留关系模式，并在链接预测和三元组分类任务上展示了其有效性和可扩展性。