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

翻译：随着网上数据量的增加,网络结构图(即网络的图表)继续演变。这个图的结构逐渐从内容基向非内容基转变。此外,网络结构图中的垃圾数据,例如超音超链接,对信息检索和连接采矿算法的速度和效率产生了不利影响。这个区域以前的工作重点是利用结构和字符串方法消除超音超链接。然而,这些方法可能错误地删除有用的链接,或在某些情况下无法探测超音超链接。在本文中,超链接的数据收集最初是用交互式爬行器构建的。超链接的语义和相关性结构随后通过语义网络方法和工具,例如DBpedia ontology等工具进行研究。最后,超音链接的清除过程是在DBpedia ontolog学上用一个解释器进行。我们的实验表明语义网络技术的准确性和能力,以删除超音超链接。</s>