Relevance Ranking and Evaluation of Search Results through Web Content Mining
G. Poonkuzhali, Raj Kumar, P.K. Sudhakar · 2012
Abstract— Nowadays, most of the people rely on web search engines to find and retrieve information. The enormous growth, diverse, dynamic and unstructured nature of web makes internet extremely difficult in searching and retrieving relevant information and in presenting query results. The aforementioned problem has given rise to the development of web content mining. This paper proposes a correlation algorithm for web content mining. In addition to relevance ranking, this algorithm also detects redundant documents. Removal of these redundant documents improves the quality of search results by providing unique relevant information Normalized discounted cumulative gain method is used for evaluating this ranking algorithm. The experimental result shows that this method ranks more than 90% of the relevant documents accurately.