Automatic ontology-based user profile learning from heterogeneous web resources in a big data context

Abstract

The Web has developed to the biggest source of information and entertainment in the world. By its size, its adaptability and flexibility, it challenged our current paradigms on information sharing in several areas. By offering everybody the opportunity to release own contents in a fast and cheap way, the Web already led to a revolution of the traditional publishing world and just now, it commences to change the perspective on advertisements. With the possibility to adapt the contents displayed on a page dynamically based on the viewer's context, campaigns launched to target rough customer groups will become an element of the past. However, this new ecosystem, that relates advertisements with the user, heavily relies on the quality of the underlying user profile. This profile has to be able to model any combination of user characteristics, the relations between its composing elements and the uncertainty that stems from the automated processing of real-world data. The work at hand describes the beginnings of a PhD project that aims to tackle those issues using a combination of data analysis, ontology engineering and processing of big data resources provided by an industrial partner. The final goal is to automatically construct and populate a profile ontology for each user identified by the system. This allows to associate these users to high-value audience segments in order to drive digital marketing.