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开放式Web上灵活专家搜索的链接数据度量
Linked Data Metrics for Flexible Expert Search on the open Web |
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| 课程网址: |
http://videolectures.net/eswc2011_stankovic_metrics/
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| 主讲教师: |
Jens Lehmann; Milan Stankovic
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| 开课单位: |
莱比锡大学
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| 开课时间: |
2011-07-07
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| 课程语种: |
英语
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| 中文简介: |
随着越来越多的用户跟踪成为可用的链接数据网络，使用这些跟踪进行专家查找成为一个有趣的挑战，特别是对于开放式创新平台。现有的专家搜索方法主要局限于一个语料库和一种特定类型的跟踪，有时甚至局限于特定的领域。我们认为，不同的专家群体使用不同的交流渠道作为他们交流和传播知识的主要手段，因此，不同类型的痕迹将与寻找不同主题的专家有关。我们提出了一种方法，使专家搜索过程（选择正确的跟踪类型和正确的专业知识假设）适应给定的专业知识主题，依靠链接的数据度量。在一个基于黄金标准的实验中，我们发现我们的度量值与专家搜索的精度和召回之间存在显著的正相关。我们还介绍了hy.semex，这是一个使用我们的关联数据度量来推荐专家搜索方法的系统，用于在hypios的开放式创新场景中寻找专家。此外，还将对用户对系统建议的满意度进行评估。
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| 课程简介: |
As more and more user traces become available as Linked Data Web, using those traces for expert finding becomes an interesting challenge, especially for the open innovation platforms. The existing expert search approaches are mostly limited to one corpus and one particular type of trace – sometimes even to a particular domain. We argue that different expert communities use different communication channels as their primary mean for communicating and disseminating knowledge, and thus different types of traces would be relevant for finding experts on different topics. We propose an approach for adapting the expert search process (choosing the right type of trace and the right expertise hypothesis) to the given topic of expertise, by relying on Linked Data metrics. In a gold standard-based experiment, we have shown that there is a significant positive correlation between the values of our metrics and the precision and recall of expert search. We also present hy.SemEx, a system that uses our Linked Data metrics to recommend the expert search approach to serve for finding experts in an open innovation scenario at hypios. The evaluation of the users’ satisfaction with the system’s recommendations is presented as well.
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| 关 键 词: |
计算机科学; Web挖掘; 链接分析
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| 课程来源: |
视频讲座网
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| 最后编审: |
2021-01-31：nkq
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| 阅读次数: |
28
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