Efficient Proposed Framework for Semantic Search Engine using New Semantic Ranking Algorithm

Abstract

The amount of information raises billions of databases every year and there is an urgent need to search for that information by a specialize tool called search engine. There are many of search engines available today, but the main challenge in these search engines is that most of them cannot retrieve meaningful information intelligently. The semantic web technology is a solution that keeps data in a readable format that helps machines to match smartly this data with related information based on meanings. In this paper, we will introduce a proposed semantic framework that includes four phases crawling, indexing, ranking and retrieval phase. This semantic framework operates over a sorting RDF by using efficient proposed ranking algorithm and enhanced crawling algorithm. The enhanced crawling algorithm crawls relevant forum content from the web with minimal overhead. The proposed ranking algorithm is produced to order and evaluate similar meaningful data in order to make the retrieval process becomes faster, easier and more accurate. We applied our work on a standard database and achieved 99 percent effectiveness on semantic performance in minimum time and less than 1 percent error rate compared with the other semantic systems.