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Instanced-Based Mapping between Thesauri and Folksonomies

机译：叙词表与民俗分类之间的基于实例的映射

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摘要

The emergence of web based systems in which users can annotate items, raises the question of the semantic interoperability between vocabularies originating from collaborative annotation processes, often called folksonomies, and keywords assigned in a more traditional way. If collections are annotated according to two systems, e.g. with tags and keywords, the annotated data can be used for instance based mapping between the vocabularies. The basis for this kind of matching is an appropriate similarity measure between concepts, based on their distribution as annotations. In this paper we propose a new similarity measure that can take advantage of some special properties of user generated metadata. We have evaluated this measure with a set of articles from Wikipedia which are both classified according to the topic structure of Wikipedia and annotated by users of the bookmarking service del.icio.us. The results using the new measure are significantly better than those obtained using standard similarity measures proposed for this task in the literature, i.e., it correlates better with human judgments. We argue that the measure also has benefits for instance based mapping of more traditionally developed vocabularies.
机译：用户可以在其中注释项目的基于Web的系统的出现提出了以下问题：源于协作注释过程的词汇（通常称为民俗分类法）与以更传统方式分配的关键字之间的语义互操作性。如果根据两个系统对集合进行注释，例如通过标签和关键字，带注释的数据可用于词汇表之间基于实例的映射。这种匹配的基础是概念之间适当的相似性度量，基于它们作为注释的分布。在本文中，我们提出了一种新的相似性度量，可以利用用户生成的元数据的某些特殊属性。我们用一套来自Wikipedia的文章评估了该措施，这些文章都根据Wikipedia的主题结构进行了分类，并由书签服务del.icio.us的用户进行了注释。使用新方法得出的结果明显优于文献中针对此任务提出的使用标准相似性度量得出的结果，即它与人类判断的相关性更好。我们认为，该措施还具有一些优势，例如可以对更多传统开发的词汇进行基于实例的映射。

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