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从链接的开放数据生成统计信息的可能解释

Generating Possible Interpretations for Statistics from Linked Open Data
课程网址: http://videolectures.net/eswc2012_paulheim_linked_data/  
主讲教师: Heiko Paulheim
开课单位: 曼海姆大学
开课时间: 2012-07-04
课程语种: 英语
中文简介:
统计数据在我们的日常生活中非常重要。每天都会出版新的统计数据，显示不同城市的生活质量，不同国家的腐败指数等。另一方面，解释这些统计数据是一项艰巨的任务。通常，统计数据只收集很少的属性，并且难以提出解释的假设，例如，为什么一度城市中的感知生活质量高于另一个城市。在本文中，我们介绍了解释LOD，这种方法使用来自Linked Open Data的数据来生成解释统计数据的假设。我们展示了一个实现的原型，并通过分析用户研究中这些假设的感知质量来比较产生假设的不同方法。
课程简介: Statistics are very present in our daily lives. Every day, new statistics are published, showing the perceived quality of living in different cities, the corruption index of different countries, and so on. Interpreting those statistics, on the other hand, is a difficult task. Often, statistics collect only very few attributes, and it is difficult to come up with hypotheses that explain, e.g., why the perceived quality of living in once city is higher than in another. In this paper, we introduce Explain-a-LOD, an approach which uses data from Linked Open Data for generating hypotheses that explain statistics. We show an implemented prototype and compare different approaches for generating hypotheses by analyzing the perceived quality of those hypotheses in a user study.
关 键 词: 统计数据; 感知质量; 假设
课程来源: 视频讲座网
最后编审: 2019-04-14：lxf
阅读次数: 79

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