Effective information retrieval and feature minimization technique for semantic web data. (January 2020)

• Record Type: Journal Article
• Authors: Saravana Kumar, C.S.; Santhosh, R.
• Is Part Of: Computers & electrical engineering. Volume 81(2020)
• Publication Date: 2020-01
• Subjects: Feature extraction -- Dimensionality reduction -- Semantic web mining -- Text mining -- Data mining -- Feature selection -- Information retrieval -- Feature vector; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.106518
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: 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.