As the volume of data on the web grows, the web structure graph, which is a
graph representation of the web, continues to evolve. The structure of this
graph has gradually shifted from content-based to non-content-based.
Furthermore, spam data, such as noisy hyperlinks, in the web structure graph
adversely affect the speed and efficiency of information retrieval and link
mining algorithms. Previous works in this area have focused on removing noisy
hyperlinks using structural and string approaches. However, these approaches
may incorrectly remove useful links or be unable to detect noisy hyperlinks in
certain circumstances. In this paper, a data collection of hyperlinks is
initially constructed using an interactive crawler. The semantic and
relatedness structure of the hyperlinks is then studied through semantic web
approaches and tools such as the DBpedia ontology. Finally, the removal process
of noisy hyperlinks is carried out using a reasoner on the DBpedia ontology.
Our experiments demonstrate the accuracy and ability of semantic web
technologies to remove noisy hyperlinks

本文提出利用语义 Web 技术对超链接进行清理，构建 Web 结构图，并探索了噪音链接对信息检索和链接挖掘算法的影响。研究表明，语义 Web 技术可以准确地清除噪音链接。