Effective information retrieval and feature minimization technique for semantic web data
Effective information retrieval and feature minimization technique for semantic web data
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DOI：
10.1016/j.compeleceng.2019.106518
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发表时间：
2020-01
期刊：
影响因子：
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通讯作者：
C. S. S. Kumar-C.-S.-S.-Kumar-46822763;R. Santhosh
中科院分区：
文献类型：
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作者：
C. S. S. Kumar-C.-S.-S.-Kumar-46822763;R. Santhosh
互联网包含结构化和非结构化数据。互联网数据的巨大流动给有效的信息检索带来了挑战。语义Web挖掘使用本体和语义结构来探索Web地址。在Web挖掘和文本挖掘中，文本特征提取对有效的信息检索起着至关重要的作用。文本处理的有效性取决于特征向量的复杂度和降维程度。本文提出了一种基于Web数据语义结构的新方法。它结合了用于数据映射和检索的特征提取和特征选择技术，包括用于有效文本映射的标准特征。该方法降低了特征向量的维数复杂度，实现了有效的信息检索。
The Internet contains both structured and unstructured data. The enormous flow of Internet data creates challenges in relation to effective information retrieval. Semantic Web Mining explores Web addresses using ontological and semantic structures. For effective information retrieval in Web Mining and Text Mining, text feature extraction plays an important role. The effectiveness of the text processing is determined by the complexity and dimensionality reduction of the feature vector. In this paper, a new approach is proposed based on the semantic structure of the Web data. It combines both feature extraction and feature selection techniques for data mapping and retrieval, involving standard features for effective text mapping. This process reduces the dimension complexity in the feature vector for effective information retrieval.