摘要翻译：
许多机器学习应用程序需要从有噪声的多关系数据中学习和推理的能力。为了解决这个问题，已经发展了几种有效的表示方法，它们既提供了一种表达领域结构规则的语言，又提供了对概率推理的原则性支持。然而，除了这两个方面之外，许多应用程序还涉及第三个方面--需要对相似性进行推理--这在现有框架中没有得到直接支持。本文介绍了概率相似性逻辑(PSL),它是一个通用的关系领域相似性联合推理框架，以原则性的方式将相似性概率推理和关系结构结合起来。PSL可以集成任何现有的特定领域的相似性度量，还支持关于实体集合之间相似性的推理。我们为PSL提供了有效的推理和学习技术，并证明了它在普通关系任务和需要相似性推理的环境中的有效性。
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英文标题：
《Probabilistic Similarity Logic》
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作者：
Matthias Brocheler, Lilyana Mihalkova, Lise Getoor
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Many machine learning applications require the ability to learn from and reason about noisy multi-relational data. To address this, several effective representations have been developed that provide both a language for expressing the structural regularities of a domain, and principled support for probabilistic inference. In addition to these two aspects, however, many applications also involve a third aspect-the need to reason about similarities-which has not been directly supported in existing frameworks. This paper introduces probabilistic similarity logic (PSL), a general-purpose framework for joint reasoning about similarity in relational domains that incorporates probabilistic reasoning about similarities and relational structure in a principled way. PSL can integrate any existing domain-specific similarity measures and also supports reasoning about similarities between sets of entities. We provide efficient inference and learning techniques for PSL and demonstrate its effectiveness both in common relational tasks and in settings that require reasoning about similarity.
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PDF链接：
https://arxiv.org/pdf/1203.3469