摘要翻译：
随着互联网的迅速发展，越来越多的信息可以在网上获得。语义Web具有许多传统搜索引擎无法处理的特性。它为每个发现的RDF或OWL格式的Web文档提取元数据，并计算文档之间的关系。提出了一种混合索引和排序的语义Web技术，该技术查找相关文档并计算文档集之间的相似度。首先，它通过使用ObjectRank技术的修改版本返回语义Web文档存储库中最相关的文档。然后，它为最相关的SWD创建子图。最后，它使用HITS算法返回这些文档的集线器和权限。我们的技术提高了结果的质量，减少了处理用户查询的执行时间。
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英文标题：
《An Enhanced Indexing And Ranking Technique On The Semantic Web》
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作者：
Ahmed Tolba and Nabila Eladawi and Mohammed Elmogy
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  With the fast growth of the Internet, more and more information is available on the Web. The Semantic Web has many features which cannot be handled by using the traditional search engines. It extracts metadata for each discovered Web documents in RDF or OWL formats, and computes relations between documents. We proposed a hybrid indexing and ranking technique for the Semantic Web which finds relevant documents and computes the similarity among a set of documents. First, it returns with the most related document from the repository of Semantic Web Documents (SWDs) by using a modified version of the ObjectRank technique. Then, it creates a sub-graph for the most related SWDs. Finally, It returns the hubs and authorities of these document by using the HITS algorithm. Our technique increases the quality of the results and decreases the execution time of processing the user's query.
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PDF链接：
https://arxiv.org/pdf/1111.6713