摘要翻译：
统计关系学习技术已经成功地应用于广泛的关系领域。在大多数这些应用中，人类设计师通过遵循一个试错轨迹来利用他们的背景知识，其中关系特征由人类工程师手动定义，在训练数据上为这些特征学习参数，结果模型被验证，并且当工程师调整特征集时循环重复。本文试图通过引入一种轻量级方法来简化大型关系域中的应用程序开发，这种方法可以在关系图的各个部分上高效地评估关系特性，这些特性一次一个地传输到关系图中。我们在两个社交媒体任务上评估了我们的方法，并证明它导致了更准确的模型，学习更快。
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英文标题：
《Structure Selection from Streaming Relational Data》
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作者：
Lilyana Mihalkova and Walaa Eldin Moustafa
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最新提交年份：
2011
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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：
  Statistical relational learning techniques have been successfully applied in a wide range of relational domains. In most of these applications, the human designers capitalized on their background knowledge by following a trial-and-error trajectory, where relational features are manually defined by a human engineer, parameters are learned for those features on the training data, the resulting model is validated, and the cycle repeats as the engineer adjusts the set of features. This paper seeks to streamline application development in large relational domains by introducing a light-weight approach that efficiently evaluates relational features on pieces of the relational graph that are streamed to it one at a time. We evaluate our approach on two social media tasks and demonstrate that it leads to more accurate models that are learned faster.
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PDF链接：
https://arxiv.org/pdf/1108.5717