An Adaptive Updating Topic Specific Web Search System Using T-Graph

Abstract

Problem statement: The main goal of a Web crawler is to collect documents that are relevant to a given topic in which the search engine specializes. These topic specific search systems typically take the whole document's content in predicting the importance of an unvisited link. But current research had proven that the document's content pointed to by an unvisited link is mainly dependent on the anchor text, which is more accurate than predicting it on the contents of the whole page. Approach: Between these two extremes, it was proposed that Treasure Graph, called T-Graph is a more effective way to guide the Web crawler to fetch topic specific documents predicted by identifying the topic boundary around the unvisited link and comparing that text with all the nodes of the T-Graph to obtain the matching node(s) and calculating the distance in the form of documents to be downloaded to reach the target documents. Results: Web search systems based on this strategy allowed crawlers and robots to update their experiences more rapidly and intelligently that can also offer speed of access and presentation advantages. Conclusion/Recommendations: The consequences of visiting a link to update a robot's experiences based on the principles and usage of T-Graph can be deployed as intelligent-knowledge Web crawlers as shown by the proposed novel Web search system architecture.