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Harnessing the semantic analysis of tag using Semantic Based Lesk Algorithm

In the field of Data retrieval, accessing web resources is frequent task. This domain is shifting radically from the amplified data growth to the way in which it is structured and retrieved across web. This explosive growth of data is the result of billions of people using the Internet and mobile devices for commerce, entertainment, social interactions and as well as the Internet of things that constantly share machine-generated data. Even with lot of research, the task of analyzing this data to extract its business values with precision still remains as a trivial issue. To address this issue, the paper presents a novel Semantic Based Lesk Algorithm (SBLA), which traces the meaning of user defined tags and categorizes the web data by means of Support Vector Machine (SVM) classifier. On comparing with existing methods, the proposed method performs well in extraction of admissible data with the better accuracy and precision as discussed in result analysis.