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.

翻译：虽然现有的知识代表性学习方法已经取得了相当大的绩效改进,但它们忽视了高端结构和大量属性信息,导致语义丰富的KG的性能不尽人意。此外,它们未能以感应方式作出预测,无法将规模扩大到大型工业图。为了解决这些问题,我们开发了一个名为KGNN的新颖框架,以充分利用知识数据,在分布式学习系统中进行代表性学习。KGNN配备了基于GNN的编码器和知识意识解码器,目的是联合探索高端结构,以细微的方式将信息归为一体,并维护KGs的关系模式。对三套数据集进行广泛的实验,以连接预测和三重分类任务,显示了KGNN框架的有效性和可扩展性。