A Bi-Dimensional User Profile to Discover Unpopular Web Sources

Romain Noël, Nicolas Malandain, Alexandre Pauchet, Laurent Vercouter, Bruno Grilhères, Stephan Brunessaux

Abstract: The discovery of new sources of information on a given topic is a prominent problem for Experts in Intelligence Analysis (EIA) who cope with the search of pages on specific and sensitive topics. Their information needs are difficult to express with queries and pages with sensitive content are difficult to find with traditional search engines as they are usually poorly indexed. We propose a double vector to model EIA's information needs, composed of DBpedia resources and keywords, both extracted from Web pages provided by the user. We also introduce a new similarity measure that is used in a Web source discovery system called DOWSER. DOWSER aims at providing users with new sources of information related to their needs without considering the popularity of a page. A series of experiments provides an empirical evaluation of the whole system.