Automated Data Extraction with Multiple Ontologies

Current search engines require an accurate yet fast automated extractor to extract relevant information from deep web for the users. Human users usually enter search queries and the search engines will then locate the desire information of interest by disambiguate the search query accordingly. The queries will then be passed on to multiple search engines for further processing. These search engines will then return the search results to the main search engine. However, data returned from these search engines are usually varied and presented in numerous formats and layouts. To extract them, we need automated extractor to filter out irrelevant information and locate the correct information. Current trends focused on using ontologies to automatically extract this information with high accuracy. To the best of our knowledge, no works have been made on using multiple ontologies (using many ontology techniques) to automatically extract information from deep webs. In this paper, we demonstrate that multiple ontologies technique can achieve higher accuracy when extracting data from the deep web. Our method outperforms existing state of the art systems and is able to robustly extract data from deep web.