Topic-specific crawling on the Web with the measurements of the relevancy context graph

One of the major problems for automatically constructed portals and information discovery systems is how to assign proper order to unvisited web pages. Topic-specific crawlers and information seeking agents should try not to traverse the off-topic areas and concentrate on links that lead to documents of interest. In this paper, we propose an effective approach based on the relevancy context graph to solve this problem. The graph can estimate the distance and the relevancy degree between the retrieved document and the given topic. By calculating the word distributions of the general and topic-specific feature words, our method will preserve the property of the relevancy context graph and reflect it on the word distributions. With the help of topic-specific and general word distribution, our crawler can measure a page's expected relevancy to a given topic and determine the order in which pages should be visited first. Simulations are also performed, and the results show that our method outperforms than the breath-first and the method using only the context graph.