Normalization of Semantic Based Web Search Engines Using Page Rank Algorithm and Hypergraph Based Clustering

In recent years, with the massive growth of web, there is an explosion of information accessible to internet users. The search logic usually tries to recover this information by exploiting many text-matching techniques. Also, traditional search engines do not have the necessary infrastructure for exploiting relation-based information that belongs to the semantic annotations for a web page. In the semantic web, each page possesses semantic metadata that record additional details concerning the web page itself. Annotations are based on classes of concepts and relations among them. We propose that relations among concepts embedded into semantic annotations can be effectively exploited to define a ranking strategy for semantic based web search engines. This approach relies on the knowledge of the user query, the underlying ontology, the hypergraph based clustering, and the web pages to be ranked. Thus, it allows us to effectively manage the search space and to reduce the complexity associated with the ranking task.