Due to continuous growth of World Wide Web (WWW), huge number of web documents is published in web servers providing plentiful information. Because of the structure of web pages and huge amount of information, retrieving of relevant information among the web sites leads very challenging task. In order to extract more useful contents automatically from the web database, this paper presents a model for annotation of web pages using semantic tagging and ranking model to effective information retrieval. To accomplish this task, relevant words are extracted after performing tag removal process, stop word removal and stemming. Then, Semantic tagging is performed to tag all the important keywords to its attribute value through a set of constrain rule list. Finally, ranking model is developed to find the most suitable elements of the attributes to map into annotated database. For experimental evaluation, user query is submitted to google engine and the relevant web results are extracted. The extracted results are then given to the proposed annotation model to construct the annotated database and evaluation is done using precision and recall.