Building Quality-based View of the Web
Enrico Triolo, Nicola Polettini, Diego Sona and Paolo Avesani
The 10th Congress of the Italian Association for Artificial Intelligence (AIIA 2007)
Roma, Italy, September 10-13, 2007
Abstract
The information available on the web is growing and the retrieval of relevant content is increasingly becoming hard. The complexity is not only concerned with semantic but also with the filtering of quality-based sources. A recent strategy to approach the overwhelming amount of information is to focus the search on a snapshot of internet, namely a web view. In this paper we present a system conceived to support the creation of a quality-based view of the web. We briefly overview the software and the functional architecture. More emphasis is devoted to the role of AI in supporting the organization of web resources in a hierarchical structure of categories. We survey our recent work in delivering a document classifier that has to deal with a twofold challenge: recommending classification of web resources when at the beginning the taxonomy is not populated and afterwards training a classifiers with few examples since usually when a category achieve a certain amount of web resources the organization policy suggests a refinement of the taxonomy. The paper includes a short description of a couple of case studies where the system has been deployed for real world applications.