In this era, search engines are acting as a vital tool for users to retrieve the necessary information in web searches. The retrieval of web page results is based on page ranking algorithms working in the search engines. It also uses the statistical based search techniques or content based information extraction from each web pages. But from the analysis of web retrieval results of Google like search engines, it is still difficult for the user to understand the inner details of each retrieved web page contents unless otherwise the user opens it separately to view the web content. This key point motivated us to propose and display an ontology based O-A-V (Object-Attribute-Value) information extraction for each web pages retrieved which will impart knowledge for the user to take the correct decision. The proposed system parses the users' natural language sentence given as a search key into O-A-V triplets and converts it as a semantically analyzed O-A-V using the inferred ontology. This conversion procedure involves various proposed algorithms and each algorithm aims to help in building the taxonomy. The ontology graph has also been displayed to the user to know the dependencies of each axiom given in his search key. The information retrieval based on this proposed method is evaluated using the precision and recall rates. © Springer International Publishing Switzerland 2014.