图 书 馆
众包关联数据质量评估Crowdsourcing Linked Data Quality Assessment |
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| 课程网址: | http://videolectures.net/iswc2013_acosta_quality_assessment/ |
| 主讲教师: | Maribel Acosta |
| 开课单位: | 卡尔斯鲁厄理工学院 |
| 开课时间: | 2013-11-28 |
| 课程语种: | 英语 |
| 中文简介: |  在本文中，我们研究了使用众包作为处理链接数据质量问题的方法，这些问题很难自动解决。我们分析了链接数据源中遇到的最常见错误，并根据它们可能适合特定形式的众包的程度对它们进行了分类。基于此分析，我们为链接数据实施了质量评估方法，该方法以不同的方式利用了人群的智慧：（i）针对研究人员和链接数据爱好者的专业人群的竞赛；补充（ii）在Amazon Mechanical Turk上发布的有偿微任务。我们根据经验评估了这种方法如何有效地发现DBpedia中的质量问题。我们还研究了如何将两种类型的人群的贡献最佳地整合到链接数据管理流程中。结果表明，两种形式的众包服务是相辅相成的，众包支持的质量评估是提高链接数据质量的一种有前途且可负担的方式。 |
| 课程简介: | In this paper we look into the use of crowdsourcing as a means to handle Linked Data quality problems that are challenging to be solved automatically. We analyzed the most common errors encountered in Linked Data sources and classified them according to the extent to which they are likely to be amenable to a specific form of crowdsourcing. Based on this analysis, we implemented a quality assessment methodology for Linked Data that leverages the wisdom of the crowds in different ways: (i) a contest targeting an expert crowd of researchers and Linked Data enthusiasts; complemented by (ii) paid microtasks published on Amazon Mechanical Turk.We empirically evaluated how this methodology could efficiently spot quality issues in DBpedia. We also investigated how the contributions of the two types of crowds could be optimally integrated into Linked Data curation processes. The results show that the two styles of crowdsourcing are complementary and that crowdsourcing-enabled quality assessment is a promising and affordable way to enhance the quality of Linked Data. |
| 关 键 词: | 质量评估; 数据链接 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-06：zyk |
| 最后编审: | 2020-11-06：zyk |
| 阅读次数: | 53 |