Web information extraction on multiple ontologies based on concept relationships upon training the user profiles

Abstract

There is a need of personalized Web information extraction. Mining vast information across the Web is not an easy task. We need to undergo various reduction techniques to remove unwanted data and to grab the useful information from the Web resources. Ontology is the best way for representing the useful information. In this paper, we have planned to develop a model based on multiple ontologies. From the constructed ontologies based on the mutual information among the concepts the taxonomy is constructed, then the relationship among the concepts is calculated. Thereby, the useful information is extracted. An algorithm is proposed for the same. The results show that the computation time for data extraction is reduced as the size of the database increases. This shows a healthy improvement for quick access of useful data from a huge information resource like the Internet.