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COALA - 相关意识主动学习链接规范
COALA – Correlation Aware Active Learning of Link Specifications |
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| 课程网址: |
http://videolectures.net/eswc2013_ngonga_ngomo_link/
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| 主讲教师: |
Marko Grobelnik, Axel-Cyrille Ngonga Ngomo
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| 开课单位: |
莱比锡大学
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| 开课时间: |
2013-07-08
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| 课程语种: |
英语
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| 中文简介: |
Link Discovery在创建遵循五个关联数据原则的知识库中发挥着核心作用。在过去几年中，已经开发了几种主动学习方法，并用于促进链接规范的监督学习。然而到目前为止，当需要来自用户的标签时，这些方法没有考虑未标记的示例之间的相关性。在本文中，我们通过提出链接规范的相关感知主动学习的概念来解决这个缺点。然后，我们提出了两种实现这一概念的通用方法。第一种方法基于图聚类，可以利用类内相关。第二种依赖于激活扩散范例，并且可以利用内部和类间的相关性。我们评估这些方法的准确性，并将它们与十种不同设置中的最新链接规范学习方法进行比较。我们的结果表明，我们的方法通过导致具有更高F分数的规范而优于现有技术。
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| 课程简介: |
Link Discovery plays a central role in the creation of knowledge bases that abide by the five Linked Data principles. Over the last years, several active learning approaches have been developed and used to facilitate the supervised learning of link specifications. Yet so far, these approaches have not taken the correlation between unlabeled examples into account when requiring labels from their user. In this paper, we address exactly this drawback by presenting the concept of the correlation-aware active learning of link specifications. We then present two generic approaches that implement this concept. The first approach is based on graph clustering and can make use of intra-class correlation. The second relies on the activation-spreading paradigm and can make use of both intra- and inter-class correlations. We evaluate the accuracy of these approaches and compare them against a state-of-the-art link specification learning approach in ten different settings. Our results show that our approaches outperform the state of the art by leading to specifications with higher F-scores.
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| 关 键 词: |
关联数据; 监督学习; 链接规范
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-04-14：lxf
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| 阅读次数: |
32
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