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

KGNN: Distributed Framework for Graph Neural Knowledge Representation