关联数据可伸缩性的挑战:概念重用导致语义衰减

Paolo Pareti, Ewan Klein, A. Barker
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引用次数: 7

摘要

越来越多的可用关联数据资源为更高级的语义Web应用程序奠定了基础。然而,它们的主要限制之一仍然是数据质量普遍较低。在本文中,我们关注的是一种质量度量,它受到可用资源增加的负面影响。我们提出了一种关联数据概念语义丰富度的度量方法,并证明了我们的假设,即一个概念被重用得越多,它的语义丰富度就越低。这是一个重要的可伸缩性问题,因为关联数据的一个核心方面是通过重用公共术语在Web上传播语义信息。我们根据语义丰富度的度量证明了我们的假设,并通过经验验证了我们的模型。最后,我们提出了解决这个可伸缩性问题的可能的未来方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Linked Data Scalability Challenge: Concept Reuse Leads to Semantic Decay
The increasing amount of available Linked Data resources is laying the foundations for more advanced Semantic Web applications. One of their main limitations, however, remains the general low level of data quality. In this paper we focus on a measure of quality which is negatively affected by the increase of the available resources. We propose a measure of semantic richness of Linked Data concepts and we demonstrate our hypothesis that the more a concept is reused, the less semantically rich it becomes. This is a significant scalability issue, as one of the core aspects of Linked Data is the propagation of semantic information on the Web by reusing common terms. We prove our hypothesis with respect to our measure of semantic richness and we validate our model empirically. Finally, we suggest possible future directions to address this scalability problem.
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