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。

KGNN：面向图神经知识表示的分布式框架

KGNN: Distributed Framework for Graph Neural Knowledge Representation

This paper considers the problem of knowledge inference on large-scale
imperfect repositories with incomplete coverage by means of embedding entities
and relations at the first attempt. We propose IIKE (Imperfect and Incomplete
Knowledge Embedding), a probabilistic model which measures the probability of
each belief, i.e. $\langle h,r,t\rangle$, in large-scale knowledge bases such
as NELL and Freebase, and our objective is to learn a better low-dimensional
vector representation for each entity ($h$ and $t$) and relation ($r$) in the
process of minimizing the loss of fitting the corresponding confidence given by
machine learning (NELL) or crowdsouring (Freebase), so that we can use $||{\bf
h} + {\bf r} - {\bf t}||$ to assess the plausibility of a belief when
conducting inference. We use subsets of those inexact knowledge bases to train
our model and test the performances of link prediction and triplet
classification on ground truth beliefs, respectively. The results of extensive
experiments show that IIKE achieves significant improvement compared with the
baseline and state-of-the-art approaches.

本研究介绍了一种名为 IIKE 的概率模型，结合对实体和关系的嵌入，以提高在大规模知识库上进行推理时的准确性，通过对不完整的知识库进行训练，得出了在链接预测和三元组分类方面比现有基准和领先方法都显著更好的结果。