Information on the web is tremendously increasing in recent years with the faster rate. This massive or voluminous data has driven intricate problems for information retrieval and knowledge management. As the data resides in a web with several forms, the Knowledge management in the web is a challenging task. Here the novel 'Semantic Web' concept may be used for understanding the web contents by the machine to offer intelligent services in an efficient way with a meaningful knowledge representation. The data retrieval in the traditional web source is focused on 'page ranking' techniques, whereas in the semantic web the data retrieval processes are based on the ‘concept based learning'. The proposed work is aimed at the development of a new framework for automatic generation of ontology and RDF to some real time Web data, extracted from multiple repositories by tracing their URI’s and Text Documents. Improved inverted indexing technique is applied for ontology generation and turtle notation is used for RDF notation. A program is written for validating the extracted data from multiple repositories by removing unwanted data and considering only the document section of the web page.