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首页> 外文期刊>International journal of signs and semiotic systems >An Efficient Approach for Ranking of Semantic Web Documents by Computing Semantic Similarity and Using HCS Clustering
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An Efficient Approach for Ranking of Semantic Web Documents by Computing Semantic Similarity and Using HCS Clustering

机译：通过计算语义相似性和使用HCS群集来进行语义Web文档的高效方法

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摘要

In today's era, with the availability of a huge amount of dynamic information available in world wide web (WWW), it is complex for the user to retrieve or search the relevant information. One of the techniques used in information retrieval is clustering, and then the ranking of the web documents is done to provide user the information as per their query. In this paper, semantic similarity score of Semantic Web documents is computed by using the semantic-based similarity feature combining the latent semantic analysis (LSA) and latent relational analysis (LRA). The LSA and LRA help to determine the relevant concepts and relationships between the concepts which further correspond to the words and relationships between these words. The extracted interrelated concepts are represented by the graph further representing the semantic content of the web document. From this graph representation for each document, the HCS algorithm of clustering is used to extract the most connected subgraph for constructing the different number of clusters which is according to the information-theoretic approach. The web documents present in clusters in graphical form are ranked by using the text-rank method in combination with the proposed method. The experimental analysis is done by using the benchmark datasets OpinRank. The performance of the approach on ranking of web documents using semantic-based clustering has shown promising results.
机译：在当今的时代，随着万维网（WWW）中可用的大量动态信息，您可以复制或搜索相关信息。在信息检索中使用的技术之一是群集，然后完成Web文档的排名以根据其查询提供用户信息。在本文中，通过使用基于语义的相似性特征来计算语义Web文档的语义相似性评分，这些相似性与潜在语义分析（LSA）和潜在关系分析（LRA）相结合。 LSA和LRA帮助确定概念之间的相关概念和关系，该概念进一步对应于这些单词之间的单词和关系。提取的相互关联的概念由图表提供了进一步表示Web文档的语义内容的图表。根据每个文档的该图形表示，群集的HCS算法用于提取最连接的子图，用于构建根据信息定理方法的不同数量的群集。在图形形式中存在于簇中的Web文档通过使用文本秩法与所提出的方法组合使用。通过使用基准数据集进行实验分析。使用基于语义群集的Web文档排名方法的性能显示了有希望的结果。

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