An Efficient Web Page Ranking for Semantic Web

Abstract

With the enormous amount of information presented on the web, the retrieval of relevant information has become a serious problem and is also the topic of research for last few years. The most common tools to retrieve information from web are search engines like Google. The Search engines are usually based on keyword searching and indexing of web pages. This approach is not very efficient as the result-set of web pages obtained include large irrelevant pages. Sometimes even the entire result-set may contain lot of irrelevant pages for the user. The next generation of search engines must address this problem. Recently, many semantic web search engines have been developed like Ontolook, Swoogle, which help in searching meaningful documents presented on semantic web. In this process the ranking of the retrieved web pages is very crucial. Some attempts have been made in ranking of semantic web pages but still the ranking of these semantic web documents is neither satisfactory and nor up to the user’s expectations. In this paper we have proposed a semantic web based document ranking scheme that relies not only on the keywords but also on the conceptual instances present between the keywords. As a result only the relevant page will be on the top of the result-set of searched web pages. We explore all relevant relations between the keywords exploring the user’s intention and then calculate the fraction of these relations on each web page to determine their relevance. We have found that this ranking technique gives better results than those by the prevailing methods.