Lifted graphical models: a survey
Lifted graphical models: a survey
复制标题
DOI：
10.1007/s10994-014-5443-2
复制
发表时间：
2011-07
期刊：
影响因子：
7.5
通讯作者：
Lilyana Mihalkova;L. Getoor
中科院分区：
文献类型：
--
作者：
Lilyana Mihalkova;L. Getoor
提升的图形模型提供了一种语言，用于表达不同类型的实体，它们的属性和它们的各种关系之间的依赖关系，以及在这种多关系域中进行概率推理的技术。在这项调查中，我们回顾了一个一般形式的提升图形模型，参数因子图，并显示了一些现有的统计关系表示映射到这种形式主义。我们讨论的推理算法，包括提升推理算法，有效地计算概率查询的答案，这样的模型。我们还回顾了从数据中学习提升图形模型的工作。对统计关系模型的需求越来越大（不管它们是用这个名字还是其他名字），因为我们被结构化和非结构化的数据淹没了，从文本中以嘈杂的方式提取实体和关系，并且需要有效地推理这些数据。我们希望，来自许多不同研究小组的想法的综合将为这个不断扩大的领域的新研究人员提供一个可访问的起点。
Lifted graphical models provide a language for expressing dependencies between different types of entities, their attributes, and their diverse relations, as well as techniques for probabilistic reasoning in such multi-relational domains. In this survey, we review a general form for a lifted graphical model, a par-factor graph, and show how a number of existing statistical relational representations map to this formalism. We discuss inference algorithms, including lifted inference algorithms, that efficiently compute the answers to probabilistic queries over such models. We also review work in learning lifted graphical models from data. There is a growing need for statistical relational models (whether they go by that name or another), as we are inundated with data which is a mix of structured and unstructured, with entities and relations extracted in a noisy manner from text, and with the need to reason effectively with this data. We hope that this synthesis of ideas from many different research groups will provide an accessible starting point for new researchers in this expanding field.