Evolutionary taxonomy construction from dynamic tag space
Evolutionary taxonomy construction from dynamic tag space
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DOI：
10.1007/s11280-011-0150-4
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发表时间：
2010-12
期刊：
影响因子：
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通讯作者：
Junjie Yao;B. Cui;G. Cong;Yuxin Huang
中科院分区：
文献类型：
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作者：
Junjie Yao;B. Cui;G. Cong;Yuxin Huang
协同标注成为当前网站的一个共同特征，方便普通用户对在线资源进行标注和表示。大量的标签集合和它们之间的关系形成了一个标签空间。在这种标签空间中，标签之间的流行度和相关性抓住了当前的社会利益。标签是自由选择的关键字，很难组织。自动提取分类法作为一种表示包容关系的分层概念结构，成为管理协作标记的一种可行方法。但是，标记会随着时间的推移而变化，因此必须将时态标记演变合并到提取的分类法中。在本文中，我们形式化了在大量标签集合上生成进化分类法的问题。生成一行分类法来反映底层标记空间的时间变化。提出的进化分类框架包括两个新的贡献。首先，我们开发了一种用于分类提取的上下文感知边缘选择算法。该方法建立在开创性的关联规则挖掘算法的基础上。其次，我们提出了几种进化分类法融合策略，使新生成的分类法与先前的分类法平滑。我们使用一个大型的现实生活中的网页标签数据集（例如，del . ci.ous）进行了广泛的性能研究。实证结果清楚地验证了所提出方法的有效性和效率。
Collaborative tagging becomes a common feature of current web sites, facilitating ordinary users to annotate and represent online resources. The large collection of tags and their relationships form a tag space. In this kind of tag space, the popularity and correlation amongst tags capture the current social interests. Tags are freely chosen keywords and difficult to organize. As a hierarchical concept structure to represent the subsumption relationships, automatically extracted taxonomies become a viable method to manage collaborative tags. However, tags change over time, and it is also imperative to incorporate the temporal tag evolution into the extracted taxonomies. In this paper, we formalize the problem of evolutionary taxonomy generation over a large collection of tags. A line of taxonomies are generated to reflect the temporal changes of underlying tag space. The proposed evolutionary taxonomy framework consists of two novel contributions. First, we develop a context-aware edge selection algorithm for taxonomy extraction. This method is built on seminal association-rule mining algorithm. Second, we propose several strategies for evolutionary taxonomy fusion, which smooths the newly generated taxonomy with prior ones. We conduct an extensive performance study using a large real-life web page tagging dataset (i.e., Del.ici.ous). The empirical results clearly verify the effectiveness and efficiency of the proposed approach.