摘要翻译：
描述实体、分类和本体的结构化和半结构化数据出现在许多领域。人们对整合来自多个来源的结构化信息有着巨大的兴趣；然而，集成结构化数据以推断复杂的公共结构是一项困难的任务，因为集成必须聚集相似的结构，同时避免在组合数据时可能出现的结构不一致。在这项工作中，我们研究结构化社会元数据的集成：由SocialWeb上的许多个人用户指定的浅层个人层次结构，并侧重于推断集成的、一致的分类法的集合。我们将此任务框架为一个具有结构约束的优化问题。我们提出了一种新的推理算法，我们称之为关系亲和传播(RAP)，它通过引入结构约束扩展了亲和传播（Frey and Dueck2007)。我们在一个真实世界的社交媒体数据集上验证了该方法，该数据集收集自photosharing网站Flickr。实验结果表明，与仅使用标准亲和传播算法的方法相比，我们提出的方法能够构造更深、更密集的结构。
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英文标题：
《Integrating Structured Metadata with Relational Affinity Propagation》
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作者：
Anon Plangprasopchok, Kristina Lerman, Lise Getoor
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最新提交年份：
2010
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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：
  Structured and semi-structured data describing entities, taxonomies and ontologies appears in many domains. There is a huge interest in integrating structured information from multiple sources; however integrating structured data to infer complex common structures is a difficult task because the integration must aggregate similar structures while avoiding structural inconsistencies that may appear when the data is combined. In this work, we study the integration of structured social metadata: shallow personal hierarchies specified by many individual users on the SocialWeb, and focus on inferring a collection of integrated, consistent taxonomies. We frame this task as an optimization problem with structural constraints. We propose a new inference algorithm, which we refer to as Relational Affinity Propagation (RAP) that extends affinity propagation (Frey and Dueck 2007) by introducing structural constraints. We validate the approach on a real-world social media dataset, collected from the photosharing website Flickr. Our empirical results show that our proposed approach is able to construct deeper and denser structures compared to an approach using only the standard affinity propagation algorithm.
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PDF链接：
https://arxiv.org/pdf/1005.4963