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.