Using Statistical and Relational Methods to Characterize Hyperlink Paths

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We describe our work in learning definitions for particular relations that exist between pairs of pages in hypertext. This work is applicable to the tasks of information extraction from the Web, and resource finding in the Web. Our approach to learning relation definitions combines a statistical text-learning method with a relational nile learner. This approach is well suited to learning in hypertext domains because its statistical component allows it to characterize text in terms of word frequencies, whereas its relational component is able to describe how neighboring documents are related to each other by the hyperlinks that connect them. We believe that this approach is applicable to other link-analysis tasks that involve characterizing relations between nodes in a graph, especially in cases where there are potentially large numbers of attributes describing the nodes and edges of the graph.