Learning probabilistic models of link structure
Learning probabilistic models of link structure
复制标题
DOI：
10.1162/jmlr.2003.3.4-5.679
复制
发表时间：
2003-05-15
影响因子：
6
通讯作者：
Taskar, B
中科院分区：
文献类型：
--
作者：
Getoor, L;Friedman, N;Taskar, B
大多数真实世界的数据是异构的，并且相互关联。例子包括网络、超文本、文献计量数据和社交网络。相比之下，大多数统计学习方法都使用“平面”数据表示，迫使我们将数据转换为丢失大部分链接结构的形式。最近引入的概率关系模型（PRM）框架通过捕获相关实体的属性之间的概率交互来包含结构化数据的对象关系性质。在本文中，我们扩展了这个框架的建模属性和链接结构本身之间的相互作用。我们的方法的一个优点是内容和关系结构的统一生成模型。我们提出了两种机制表示概率分布的链接结构：参考不确定性和存在的不确定性。我们描述了使用每个模型的适当条件，并提出了每个学习算法。我们目前的实验结果表明，学习的模型可以用来预测链接结构，而且，观察到的链接结构可以用来提供更好的预测模型中的属性。
Most real-world data is heterogeneous and richly interconnected. Examples include the Web, hypertext, bibliometric data and social networks. In contrast, most statistical learning methods work with "flat" data representations, forcing us to convert our data into a form that loses much of the link structure. The recently introduced framework of probabilistic relational models (PRMs) embraces the object-relational nature of structured data by capturing probabilistic interactions between attributes of related entities. In this paper, we extend this framework by modeling interactions between the attributes and the link structure itself. An advantage of our approach is a unified generative model for both content and relational structure. We propose two mechanisms for representing a probabilistic distribution over link structures: reference uncertainty and existence uncertainty. We describe the appropriate conditions for using each model and present learning algorithms for each. We present experimental results showing that the learned models can be used to predict link structure and, moreover, the observed link structure can be used to provide better predictions for the attributes in the model.