SEARCH ENGINE OPTIMIZATION USING FUZZY ONTOLOGY GENERATION FRAMEWORK

Abstract

The main objective of this project is to optimize the search engine so that it retrieves only the relevant documents requested by the user using FOGA.The system is wholly based on machine learning rather than human interpretation for gathering relevant information. This is a semantic web-based information retrieval system to support scholarly activities performed in the semantic web environment. This system does machine learning when the particular keyword is specified by the user. Rather than retrieval of information or documents based on the occurrence of the particular keywords, relationships are formed between those set of keywords. And the machine learns what is the need of the user and retrieves only those information that are relevant. This system uses the Fuzzy Ontology Generation frAmework (FOGA) would be useful to construct ontology from insecurity data as it can represent insecurity information and construct a concept hierarchy automatically. Through this system the user fatigue in extracting the necessary information from the web is reduced and computers can automatically harness the enormous network of information and services on the web.