Ontology emergence from folksonomies

Kaipeng Liu, Binxing Fang, Weizhe Zhang
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引用次数: 24

Abstract

The folksonomies built from the large-scale social annotations made by collaborating users are perfect data sources for bootstrapping Semantic Web applications. In this paper, we develop an ontology induction approach to harvest the emergent semantics from the folksonomies. We propose a latent subsumption hierarchy model to uncover the implicit structure of tag space and develop our ontology induction approach on basis of this model. We identify tag subsumptions with a set-theoretical approach and model the tag space as a tag subsumption graph. While turning this graph into a concept hierarchy, we address the problem of inconsistent subsumptions and propose a random walk based tag generality ranking procedure to settle it. We propose an agglomerative hierarchical clustering algorithm utilizing the result of tag generality ranking to generate the concept hierarchy. We conduct experiments on the Delicious dataset. The results of both qualitative and quantitative evaluation demonstrate the effectiveness of the proposed approach.
从大众分类法中产生的本体
协作用户所做的大规模社会注释构建的大众分类法是引导语义Web应用程序的完美数据源。在本文中,我们开发了一种本体归纳方法来从大众分类法中获取紧急语义。为了揭示标签空间的隐式结构,我们提出了一个潜在的包含层次模型,并在此基础上发展了本体归纳方法。我们用集合理论的方法识别标签包容,并将标签空间建模为标签包容图。在将该图转化为概念层次结构的同时,我们解决了不一致假设的问题,并提出了基于随机行走的标签一般性排序过程来解决这个问题。本文提出了一种利用标签通用性排序结果生成概念层次的聚类算法。我们在Delicious数据集上进行实验。定性和定量评价的结果都证明了所提出方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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