An Enhanced Indexing And Ranking Technique On The Semantic Web
  With the fast growth of the Internet, more and more information is available
on the Web. The Semantic Web has many features which cannot be handled by using
the traditional search engines. It extracts metadata for each discovered Web
documents in RDF or OWL formats, and computes relations between documents. We
proposed a hybrid indexing and ranking technique for the Semantic Web which
finds relevant documents and computes the similarity among a set of documents.
First, it returns with the most related document from the repository of
Semantic Web Documents (SWDs) by using a modified version of the ObjectRank
technique. Then, it creates a sub-graph for the most related SWDs. Finally, It
returns the hubs and authorities of these document by using the HITS algorithm.
Our technique increases the quality of the results and decreases the execution
time of processing the user's query.
