Massive digital documents on Internet leading to use e-learning, and it becomes an emerging field of research due to the massive growth of internet users. E-learning requires suitable document ranking method to avoid navigating to the next Search Engine Result Page (SERP) frequently. The existing document ranking methods are lacking to rank the documents independently based on the conceptual contents. This paper proposes a novel method for ranking the documents independently based on the different classification of term it contains. In this approach, the terms are classified into five categories such as (1) direct query term, (2) expanded terms, (3) semantically related term, (4) supporting terms and (5) stop words. The query has been expanded using domain ontology to acquire more semantic terms for better understanding of user query. The semantic weight has been applied independently over different categories of terms in a document for ranking. The document with the highest augmented value in each category of terms has been ranked first. Remaining documents are ranked in the same way and are arranged in the descending order. The WordNet tool is utilized as a knowledge base and Wu and Palmer semantic distance method have applied for measuring semantic distance between the query and document terms for ranking the terms. The experiments show that the performance of the proposed document ranking method for e-learning retrieved better document compared with existing document ranking methods.

中文翻译：

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使用基于语义的文档术语分类进行电子学习的独立文档排名

Internet上的大量数字文档导致使用电子学习，并且由于Internet用户的大量增长，它成为一个新兴的研究领域。电子学习需要适当的文档排名方法，以避免频繁导航到下一个搜索引擎结果页面（SERP）。现有的文档排名方法缺乏基于概念内容对文档进行独立排名的方法。本文提出了一种新的方法，可以根据文档所含术语的不同分类对文档进行独立排名。在这种方法中，术语被分为五类，例如（1）直接查询术语，（2）扩展术语，（3）语义相关术语，（4）支持术语和（5）停用词。使用域本体扩展了查询，以获取更多语义术语，以更好地理解用户查询。语义权重已独立地应用于文档中用于排名的不同类别的术语。在每个术语类别中增值最高的文档已排名第一。其余文档以相同的方式排序，并以降序排列。WordNet工具被用作知识库，Wu和Palmer语义距离方法已应用于测量查询和文档术语之间的语义距离，以对术语进行排名。实验表明，与现有的文档排名方法相比，所提出的用于电子学习的文档排名方法的性能更好。其余文档以相同的方式排序，并以降序排列。WordNet工具被用作知识库，Wu和Palmer语义距离方法已应用于测量查询和文档术语之间的语义距离，以对术语进行排名。实验表明，与现有的文档排名方法相比，所提出的用于电子学习的文档排名方法的性能更好。其余文档以相同的方式排序，并以降序排列。WordNet工具被用作知识库，Wu和Palmer语义距离方法已应用于测量查询和文档术语之间的语义距离，以对术语进行排名。实验表明，与现有的文档排名方法相比，所提出的用于电子学习的文档排名方法的性能更好。