A topic-specific crawling strategy based on semantics similarity

With the Internet growing exponentially, search engines are encountering unprecedented challenges. A focused search engine selectively seeks out web pages that are relevant to user topics. Determining the best strategy to utilize a focused search is a crucial and popular research topic. At present, the rank values of unvisited web pages are computed by considering the hyperlinks (as in the PageRank algorithm), a Vector Space Model and a combination of them, and not by considering the semantic relations between the user topic and unvisited web pages. In this paper, we propose a concept context graph to store the knowledge context based on the user's history of clicked web pages and to guide a focused crawler for the next crawling. The concept context graph provides a novel semantic ranking to guide the web crawler in order to retrieve highly relevant web pages on the user's topic. By computing the concept distance and concept similarity among the concepts of the concept context graph and by matching unvisited web pages with the concept context graph, we compute the rank values of the unvisited web pages to pick out the relevant hyperlinks. Additionally, we constitute the focused crawling system, and we retrieve the precision, recall, average harvest rate, and F-measure of our proposed approach, using Breadth First, Cosine Similarity, the Link Context Graph and the Relevancy Context Graph. The results show that our proposed method outperforms other methods.