A hybrid method for improving the SQD-PageRank algorithm

The PageRank algorithm is used in the Google search engine to calculate a single list of popularity scores for each page in the Web. These popularity scores are used to rank query results when presented to the user. PageRank assigns to a page a score proportional to the number of times a random surfer would visit that page, if it surfed indefinitely from page to page, following all outlinks from a page with equal probability. Thereupon, several algorithms are introduced to improve the last one. In this paper, we introduce a more intelligent surfer model based on combining ontology, web contents and PageRank. Firstly, we propose a relevance measure of a web page relative to a multiple-term query. Then, we develop our performed intelligent surfer model. Efficient execution of our algorithm in a local database is performed. Results show that our algorithm significantly outperforms the existing algorithms in the quality of the pages returned, while remaining efficient enough to be used in today´s large search engines.