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Link classification with probabilistic graphs

机译：链接分类与概率图

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摘要

The need to deal with the inherent uncertainty in real-world relational or networked data leads to the proposal of new probabilistic models, such as probabilistic graphs. Every edge in a probabilistic graph is associated with a probability whose value represents the likelihood of its existence, or the strength of the relation between the entities it connects. The aim of this paper is to propose two machine learning techniques for the link classification problem in relational data exploiting the probabilistic graph representation. Both the proposed methods will exploit a language-constrained reachability method to infer the probability of possible hidden relationships that may exists between two nodes in a probabilistic graph. Each hidden relationships between two nodes may be viewed as a feature (or a factor), and its corresponding probability as its weight, while an observed relationship is considered as a positive instance for its corresponding link label. Given a training set of observed links, the first learning approach is to use a propositionalization technique adopting a L2-regularized Logistic Regression to learn a model able to predict unobserved link labels. Since in some cases the edges' probability may be not known in advance or they could not be precisely defined for a classification task, the second xposed approach is to exploit the inference method and to use a mean squared technique to learn the edges' probabilities. Both the proposed methods have been evaluated on real world data sets and the corresponding results proved their validity.
机译：处理现实世界中的关系或网络数据中固有的不确定性的需求导致提出了新的概率模型，例如概率图。概率图中的每个边都与一个概率相关联，该概率的值表示其存在的可能性或它所连接的实体之间关系的强度。本文的目的是提出两种机器学习技术，以利用概率图表示来解决关系数据中的链接分类问题。两种提出的方​​法都将利用语言受限的可达性方法来推断概率图中两个节点之间可能存在的潜在隐藏关系的可能性。两个节点之间的每个隐藏关系都可以被视为一个特征（或一个因素），其对应的概率可以被视为其权重，而观察到的关系则被视为其对应的链接标签的肯定实例。给定一组观察到的链接的训练，第一种学习方法是使用采用L2正则化Logistic回归的命题化技术来学习能够预测未观察到的链接标签的模型。由于在某些情况下可能无法预先知道边缘的概率，或者无法为分类任务精确定义边缘的概率，因此第二种放置方法是利用推理方法并使用均方技术来学习边缘的概率。两种提出的方​​法均已在现实世界的数据集上进行了评估，相应的结果证明了它们的有效性。

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