Incrementally Updating Concept Context Graph (CCG) for Focused Web Crawling Based on FCA

Focused Web crawler collects relevant Web pages of interested topics from the Internet. Most searchers have studied strategy based on an initial model to gather as many relevant Web pages as possible in the focused Web crawling. However, Web information continually change over time, the initial model representing outdated information canpsilat reflect userpsilas interested topics rightly. In this paper, we proposed a model named Concept Context Graph (CCG) based on Formal Concept Analysis (FCA) and updated it to get more relevant Web pages. We had gotten inspiration from incremental idea for updating concept lattice. But our task is not updating concept lattice but updating CCG associated with a certain core concept. We took an unvisited page as an Incremental Concept (IC), judged the layer at which the IC located in the CCG by the attributes of concept and inserted this IC into CCG by the semantic similarity between core concept and incremental concept. As far as we know, it is the first literature on updating the initial model to get more relevant Web pages in the focused Web crawling.