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Ontology based fuzzy classification of web documents for semantic information retrieval

机译:基于本体的Web文档语义信息模糊分类

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Several approaches have been introduced in the field of information retrieval. Although these approaches are effective but sometimes they are not able to provide accurate information to the user. In this paper an ontology based approach of information retrieval has been presented that uses fuzzy set of various documents for a specific domain. An algorithm for fuzzy based classification of web documents is proposed to create semantic index. The proposed algorithm differs from others as: it utilizes K-means clustering algorithm to find semantically similar terms and domain ontology as well. The retrieved results would always be semantic as they are limited to a particular threshold of classified range.
机译:在信息检索领域已经引入了几种方法。尽管这些方法有效,但有时它们无法向用户提供准确的信息。在本文中,提出了一种基于本体的信息检索方法,该方法针对特定领域使用各种文档的模糊集。提出了一种基于模糊的Web文档分类算法,以建立语义索引。该算法与其他算法的不同之处在于:它利用K-means聚类算法来找到语义相似的术语和领域本体。检索到的结果将始终是语义的,因为它们仅限于分类范围的特定阈值。

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