基于民俗学的协作学习Folksonomy-based collabulary learning |
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课程网址: | http://videolectures.net/iswc08_balby_marinho_fbcl/ |
主讲教师: | Leandro Balby Marinho |
开课单位: | 希尔德斯海姆大学 |
开课时间: | 2008-11-24 |
课程语种: | 英语 |
中文简介: | 社交标记系统的日益普及有望减轻知识瓶颈,从而减缓语义Web的完全实现,因为这些系统便宜,可扩展,可扩展并能快速响应用户需求。然而,为了知识工作流程,人们需要在大众分类法的未经处理的性质和领域专家的受控词汇表之间找到折衷方案。在本文中,我们通过首先设计一种方法来解决这一问题,该方法自动将民众分类与领域专家本体相结合,从而产生丰富的分类。然后,我们引入了一种基于频繁项集挖掘的新算法,该算法有效地学习了丰富的分类中存在的概念的本体。此外,我们提出了一个新的本体评估基准,用于信息发现的背景,因为这是在社会标签系统中使用本体,以定量评估我们的方法的主要动机之一。我们对实际数据进行实验,并凭经验证明了我们方法的有效性。 |
课程简介: | The growing popularity of social tagging systems promises to alleviate the knowledge bottleneck that slows the full materialization of the Semantic Web, as these systems are cheap, extendable, scalable and respond quickly to user needs. However, for the sake of knowledge workflow, one needs to find a compromise between the ungoverned nature of folksonomies and the controlled vocabulary of domain-experts. In this paper, we address this concern by first devising a method that automatically combines folksonomies with domain-expert ontologies resulting in an enriched folksonomy. We then introduce a new algorithm based on frequent itemsets mining that efficiently learns an ontology over the concepts present in the enriched folksonomy. Moreover, we propose a new benchmark for ontology evaluation, which is used in the context of information finding, since this is one of the leading motivations for using ontologies in social tagging systems, to quantitatively assess our method. We conduct experiments on real data and empirically show the effectiveness of our approach. |
关 键 词: | 社交标记系统; 用户需求; 频繁项集挖掘 |
课程来源: | 视频讲座网 |
最后编审: | 2019-04-27:cwx |
阅读次数: | 28 |