The study of machine learning-based logical query-answering enables reasoning
with large-scale and incomplete knowledge graphs. This paper further advances
this line of research by considering the uncertainty in the knowledge. The
uncertain nature of knowledge is widely observed in the real world, but
\textit{does not} align seamlessly with the first-order logic underpinning
existing studies. To bridge this gap, we study the setting of soft queries on
uncertain knowledge, which is motivated by the establishment of soft constraint
programming. We further propose an ML-based approach with both forward
inference and backward calibration to answer soft queries on large-scale,
incomplete, and uncertain knowledge graphs. Theoretical discussions present
that our methods share the same complexity as state-of-the-art inference
algorithms for first-order queries. Empirical results justify the superior
performance of our approach against previous ML-based methods with number
embedding extensions.

该研究通过考虑不确定性知识进一步推进基于机器学习的逻辑查询回答的研究，并提出了一种基于机器学习的方法以回答大规模、不完整和不确定的知识图上的软查询。

不确定知识图上的软推理

Soft Reasoning on Uncertain Knowledge Graphs

Large-scale knowledge graphs are increasingly common in many domains. Their
large sizes often exceed the limits of systems storing the graphs in a
centralized data store, especially if placed in main memory. To overcome this,
large knowledge graphs need to be partitioned into multiple sub-graphs and
placed in nodes in a distributed system. But querying these fragmented
sub-graphs poses new challenges, such as increased communication costs, due to
distributed joins involving cut edges. To combat these problems, a good
partitioning should reduce the edge cuts while considering a given query
workload. However, a partitioned graph needs to be continually re-partitioned
to accommodate changes in the query workload and maintain a good average
processing time. In this paper, an adaptive partitioning method for large-scale
knowledge graphs is introduced, which adapts the partitioning in response to
changes in the query workload. Our evaluation demonstrates that the performance
of processing time for queries is improved after dynamically adapting the
partitioning of knowledge graph triples.

本篇文章提出了一种自适应分区方法，可应对大规模知识图谱中查询工作量变化的需求，通过动态适应知识图谱三元组的分区，可以提高查询处理时间的性能。

AWAPart：适应工作负载的知识图谱分区

AWAPart: Adaptive Workload-Aware Partitioning of Knowledge Graphs

Graph neural networks (GNNs) have achieved superior performance in various
applications, but training dedicated GNNs can be costly for large-scale graphs.
Some recent work started to study the pre-training of GNNs. However, none of
them provide theoretical insights into the design of their frameworks, or clear
requirements and guarantees towards their transferability. In this work, we
establish a theoretically grounded and practically useful framework for the
transfer learning of GNNs. Firstly, we propose a novel view towards the
essential graph information and advocate the capturing of it as the goal of
transferable GNN training, which motivates the design of EGI (Ego-Graph
Information maximization) to analytically achieve this goal. Secondly, when
node features are structure-relevant, we conduct an analysis of EGI
transferability regarding the difference between the local graph Laplacians of
the source and target graphs. We conduct controlled synthetic experiments to
directly justify our theoretical conclusions. Comprehensive experiments on two
real-world network datasets show consistent results in the analyzed setting of
direct-transfering, while those on large-scale knowledge graphs show promising
results in the more practical setting of transfering with fine-tuning.

该研究提出了一种基于理论基础和实用性的 GNN 传递学习框架，首先提出了一种新颖的图信息视图，并提出通过 EGI (Ego-Graph Information maximization) 来分析实现传递学习目标，其次，当节点特征是结构相关的时，我们对 EGI 的可转移性进行了分析，综合两个真实数据集的实验结果表明具有传递性，在大规模知识图谱的转移学习中表现出良好的效果。

基于自我图信息最大化的图神经网络迁移学习

Transfer Learning of Graph Neural Networks with Ego-graph Information  Maximization

We study the problem of learning to reason in large scale knowledge graphs
(KGs). More specifically, we describe a novel reinforcement learning framework
for learning multi-hop relational paths: we use a policy-based agent with
continuous states based on knowledge graph embeddings, which reasons in a KG
vector space by sampling the most promising relation to extend its path. In
contrast to prior work, our approach includes a reward function that takes the
accuracy, diversity, and efficiency into consideration. Experimentally, we show
that our proposed method outperforms a path-ranking based algorithm and
knowledge graph embedding methods on Freebase and Never-Ending Language
Learning datasets.

本文提出了一种基于强化学习的多跳关系路径学习框架，利用知识图谱嵌入、采样方式和奖励函数提高路径推理的精度、多样性和效率，并在 Freebase 和 Never-Ending Language Learning 数据集上展示了其优异性能。