We introduce a conceptually simple and effective method to quantify the
similarity between relations in knowledge bases. Specifically, our approach is
based on the divergence between the conditional probability distributions over
entity pairs. In this paper, these distributions are parameterized by a very
simple neural network. Although computing the exact similarity is in-tractable,
we provide a sampling-based method to get a good approximation. We empirically
show the outputs of our approach significantly correlate with human judgments.
By applying our method to various tasks, we also find that (1) our approach
could effectively detect redundant relations extracted by open information
extraction (Open IE) models, that (2) even the most competitive models for
relational classification still make mistakes among very similar relations, and
that (3) our approach could be incorporated into negative sampling and softmax
classification to alleviate these mistakes. The source code and experiment
details of this paper can be obtained from
this https URL

本文提出了一种基于条件概率分布的相似性量化方法，采用简单的神经网络参数化分布，并给出了一个采样的实现方法。实验证明，该方法与人类判断结果相关度显著高，并能有效地检测 Open IE 模型提取的冗余关系和分类中的错误，并可用于负采样和 softmax 分类以减轻错误。

用事实分布量化关系相似度

Quantifying Similarity between Relations with Fact Distribution

Current evaluation functions for heuristic planning are expensive to compute.
In numerous planning problems these functions provide good guidance to the
solution, so they are worth the expense. However, when evaluation functions are
misguiding or when planning problems are large enough, lots of node evaluations
must be computed, which severely limits the scalability of heuristic planners.
In this paper, we present a novel solution for reducing node evaluations in
heuristic planning based on machine learning. Particularly, we define the task
of learning search control for heuristic planning as a relational
classification task, and we use an off-the-shelf relational classification tool
to address this learning task. Our relational classification task captures the
preferred action to select in the different planning contexts of a specific
planning domain. These planning contexts are defined by the set of helpful
actions of the current state, the goals remaining to be achieved, and the
static predicates of the planning task. This paper shows two methods for
guiding the search of a heuristic planner with the learned classifiers. The
first one consists of using the resulting classifier as an action policy. The
second one consists of applying the classifier to generate lookahead states
within a Best First Search algorithm. Experiments over a variety of domains
reveal that our heuristic planner using the learned classifiers solves larger
problems than state-of-the-art planners.

本文提出了一种基于机器学习的启发式计划搜索控制方法，通过学习搜索启发式计划的控制策略，实现了减少计算节点评估的目的，并通过两种方法（使用分类器作为行动策略和将分类器应用于生成前瞻状态）有效提高搜索效率，解决了现有启发式计划算法在可伸缩性方面的不足。

使用关系决策树扩展启发式规划

Scaling up Heuristic Planning with Relational Decision Trees

Network sampling is integral to the analysis of social, information, and
biological networks. Since many real-world networks are massive in size,
continuously evolving, and/or distributed in nature, the network structure is
often sampled in order to facilitate study. For these reasons, a more thorough
and complete understanding of network sampling is critical to support the field
of network science. In this paper, we outline a framework for the general
problem of network sampling, by highlighting the different objectives,
population and units of interest, and classes of network sampling methods. In
addition, we propose a spectrum of computational models for network sampling
methods, ranging from the traditionally studied model based on the assumption
of a static domain to a more challenging model that is appropriate for
streaming domains. We design a family of sampling methods based on the concept
of graph induction that generalize across the full spectrum of computational
models (from static to streaming) while efficiently preserving many of the
topological properties of the input graphs. Furthermore, we demonstrate how
traditional static sampling algorithms can be modified for graph streams for
each of the three main classes of sampling methods: node, edge, and
topology-based sampling. Our experimental results indicate that our proposed
family of sampling methods more accurately preserves the underlying properties
of the graph for both static and streaming graphs. Finally, we study the impact
of network sampling algorithms on the parameter estimation and performance
evaluation of relational classification algorithms.

本文提出了一个网络抽样的框架，此框架能从静态到流式的完整的计算模型范围内，基于图诱导设计了一系列通用的采样方法，并证明此系列采样方法可以更准确地保留输入图的拓扑特性，研究了网络采样算法对关系分类算法参数估计和性能评估的影响。