a framework for mining meaningful usage patterns within a semantically enhanced web portal
Semantic Web (SW) is a new trend in the evolution of the current Web aimed at extending its basic functionalities by providing computer-readable semantic meta-data about the Web content. The meta-data is typically organized into a domain ontology where key concepts and relations from the domain appear. The benefits of such a representation are manifold: a more topical information seeking process, better content adaptation and higher interoperability even on the current, still largely syntactical, Web, to name only a few. As the SW is, arguably, the future of the Web, it is only too natural that Web mining, i.e., the application of data mining techniques to web-related data, tackles the processing semantically annotated data. In this context, we study the detecting of typical navigation scenarios on an ontology-powered Web portal, i.e., an instance of usage mining on the SW. In the present paper, we tackle the fundamental aspects of the underlying mining problem and clarify the impact a fully-fledged ontology has on the data and pattern languages. Indeed, current ontology-aware mining approaches tend to limit their scope to the core conceptual hierarchy (taxonomy) of an ontology whereas in a realistic settings there will be a lot more knowledge in the ontology, in particular, on semantic relations between domain concepts, the way they instantiate into links between content objects, etc. We show that reflecting domain relations in the navigation patterns results in a new pattern structure that combines elements from sequential, generalized and graph pattern mining and therefore requires a dedicated mining strategy. After characterizing the underlying pattern space, we describe a dedicated level-wise mining method and present some empirical evidence of its viability.
ontologies web adaptation sequential pattern mining