Due to Web size and diversity of information, relevant information gathering on the Web turns out to be a highly complex task. The main problem with most information retrieval approaches is neglecting pages’ context, given their inner deficiency: search engines are based on keyword indexing, which cannot capture context. Considering restricted domains, taking into account contexts, with the use of domain ontology, may lead to more relevant and accurate information gathering. In the last years, we have conducted research with this hypothesis, and proposed an agent- and ontology-based restricted-domain cooperative information gathering approach accordingly, that can be instantiated in information gathering systems for specific domains, such as academia, tourism, etc. In this chapter, the authors present this approach, a generic software architecture, named AGATHE-2, which is a full-fledged scalable multi-agent system. Besides offering an in-depth treatment for these domains due to the use of domain ontology, this new version uses machine learning techniques over linguistic information in order to accelerate the knowledge acquisition necessary for the task of information extraction over the Web pages. AGATHE-2 is an agent and ontology-based system that collects and classifies relevant Web pages about a restricted domain, using the BWI (Boosted Wrapper Induction), a machine-learning algorithm, to perform adaptive information extraction.
THE METHOD OF AUTOMATED BUILDING OF DOMAIN ONTOLOGY