An Integrated Approach for Knowledge discovery and Information retrieval on Web

Abstract

This Paper focuses on the integration of web information and subsequent knowledge relationship discovery within the integrated web data. The problem of information overload on the Internet has brought new attention to the ideas of filtering information on internet. Knowledge Discovery is often used for analysis of large amounts of web data and enables addressing a number of tasks that arise in Semantic Web and require scalable solutions. The World Wide Web and related web Information resources no arguably stand as the best-preferred medium for distributing information. It introduces various approaches to knowledge relation discovery like model creation, exact comparison and dynamic comparison. The nature of the web and the mass of valuable web information it holds, poses an ideal stage for applying data mining techniques for efficient discovery of knowledge from the World Wide Web. The eagerness shown by various research communities has made web based data mining (Web Mining) a rich mixture of different technologies. Therefore the heterogeneity in the area of web mining is as high as web itself. Our objective is to design an approach for information filtering, a general approach to personalized information filtering. Social Information filtering essentially automates the process of “word-of-mouth” recommendations: items are recommended to a user based upon values assigned by other people with similar taste. The system determines which users have similar taste via standard formulas for computing statistical correlations. The World Wide Web (WWW) provides a vast source of information. Technique for making personalized recommendations from any type of database to a user based on similarities between the interest profile of that user and those of other users. Recent years have seen the explosive growth of the sheer volume of information.