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首页> 外文期刊>Expert systems with applications >A Collaborative Desktop Tagging System For Group Knowledge Management Based On Concept Space
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A Collaborative Desktop Tagging System For Group Knowledge Management Based On Concept Space

机译：基于概念空间的团体知识管理协同桌面标记系统

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The advent of internet has led to a significant growth in the amount of information available, resulting in information overload, i.e. individuals have too much information to make a decision. To resolve this problem, collaborative tagging systems form a categorization called folksonomy in order to organize web resources. A folksonomy aggregates the results of personal free tagging of information and objects to form a categorization structure that applies utilizes the collective intelligence of crowds. Folksonomy is more appropriate for organizing huge amounts of information on the Web than traditional taxonomies established by expert cataloguers. However, the attributes of collaborative tagging systems and their folksonomy make them impractical for organizing resources in personal environments.rnThis work designs a desktop collaborative tagging (DCT) system that enables collaborative workers to tag their documents. This work proposes an application in patent analysis based on the DCT system. Folksonomy in DCT is built by aggregating personal tagging results, and is represented by a concept space. Concept spaces provide synonym control, tag recommendation and relevant search. Additionally, to protect privacy of authors and to decrease the transmission cost, relations between tagged and untagged documents are constructed by extracting document's features rather than adopting the full text.rnExperimental results reveal that the adoption rate of recommended tags for new documents increases by 10% after users have tagged five or six documents. Furthermore, DCT can recommend tags with higher adoption rates when given new documents with similar topics to previously tagged ones. The relevant search in DCT is observed to be superior to keyword search when adopting frequently used tags as queries. The average precision, recall, and F-measure of DCT are 12.12%, 23.08%, and 26.92% higher than those of keyword searching.rnDCT allows a multi-faceted categorization of resources for collaborative workers and recommends tags for categorizing resources to simplify categorization easier. Additionally, DCT system provides relevance searching, which is more effective than traditional keyword searching for searching personal resources.
机译：互联网的出现导致可用信息量显着增长，导致信息过载，即个人拥有太多信息，无法做出决定。为了解决此问题，协作标记系统形成了一个名为民俗分类法的分类，以便组织网络资源。民间疗法将信息和对象的个人免费标签的结果汇总在一起，从而形成一种利用人群的集体智慧进行应用的分类结构。与专家编目员建立的传统分类法相比，Folksonomy更适合于在Web上组织大量信息。但是，协作标记系统的属性及其民俗化使其在组织个人环境中的资源时不切实际。这项工作设计了一个桌面协作标记（DCT）系统，该系统使协作工作者可以标记其文档。这项工作提出了在基于DCT系统的专利分析中的应用。 DCT中的Folksonomy是通过汇总个人标签结果而构建的，并以概念空间表示。概念空间提供同义词控制，标签推荐和相关搜索。此外，为了保护作者的隐私并降低传输成本，通过提取文档的特征而不是采用全文来构造带标签的文档和未带标签的文档之间的关系。实验结果表明，新文档的推荐标签的采用率提高了10％用户标记了五六个文档之后。此外，当给与具有与先前标记主题相似主题的新文档时，DCT可以推荐采用率更高的标记。当采用常用标签作为查询时，可以发现DCT中的相关搜索优于关键字搜索。 DCT的平均精度，召回率和F度量比关键字搜索分别高12.12％，23.08％和26.92％.rnDCT允许协作工作者对资源进行多方面的分类，并建议对资源进行分类的标签以简化分类更轻松。另外，DCT系统提供了相关性搜索，比传统的关键字搜索更有效地搜索个人资源。

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