论文标题
KGNN：图形神经知识表示的分布式框架
KGNN: Distributed Framework for Graph Neural Knowledge Representation
论文作者
论文摘要
知识表示学习通常被采用以将知识图（KG）纳入各种在线服务。尽管现有的知识表示学习方法已经取得了很大的提高，但它们忽略了高阶结构和丰富的属性信息，从而导致语义丰富的KGS的性能不令人满意。此外，他们无法以归纳方式进行预测，并且无法扩展到大型工业图。为了解决这些问题，我们开发了一个名为KGNN的新颖框架，以充分利用了在分布式学习系统中的代表性学习的知识数据。 KGNN配备了基于GNN的编码器和知识意识解码器，该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，并保留KGS中的关系模式。在三个数据集上进行链接预测和三重态分类任务的大量实验证明了KGNN框架的有效性和可扩展性。
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.
