Semantic Web Technology and Data Mining for Personalized System to Online E-Commerce

Abstract

News-papers, blogs, and web-pages are a rich and diverse source of textual information. However, the information contained in these sources cannot be physically extracted, verified, and indexed, mainly because it comes in a massive size. Moreover, the extraction of some information sometimes requires specific knowledge or technical background. This is the case in the news domain where we need to extract the relevant news from a lot of available information. In order to scale knowledge extraction to the large size of available textual information, and build extractors specific to a certain field various techniques are applied over the unstructured data so that it can be made available to the users. This could help the researchers and the news readers or users to find applicable information in less time and with great ease. This study aims to review all the approaches and techniques done so far, for the information retrieval, search capability and its analysis and it also proposed an idea for better searching that reduces the time complexity to extract the data and also reduces human intervention. This is a better idea to put forward which also helps in filtering of irrelevant data and thus integrates only the relevant data to create a better space for the news data.