In the last decade, new approaches focused on modelling uncertainty over complex relational data have been developed. In this paper one of the most promising of such approaches, known as Probabilistic Relational Models (PRMs), has been investigated and extended in order to measure and include uncertainty over relationships. Our extension, called PRMs with Relational Uncertainty, has been evaluated on real-data for web document classification purposes. Experimental results shown the potentiality of the proposed methods of capturing the real "strength" of relationships and the capacity of including this information into the probability model
Fersini, E., Archetti, F., Messina, V. (2009). Probabilistic relational models with relational uncertainty: an early study on web classification. In 2009 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 3 (pp.139-142). IEEE [10.1109/WI-IAT.2009.249].
Probabilistic relational models with relational uncertainty: an early study on web classification
FERSINI, ELISABETTA;ARCHETTI, FRANCESCO ANTONIO;MESSINA, VINCENZINA
2009
Abstract
In the last decade, new approaches focused on modelling uncertainty over complex relational data have been developed. In this paper one of the most promising of such approaches, known as Probabilistic Relational Models (PRMs), has been investigated and extended in order to measure and include uncertainty over relationships. Our extension, called PRMs with Relational Uncertainty, has been evaluated on real-data for web document classification purposes. Experimental results shown the potentiality of the proposed methods of capturing the real "strength" of relationships and the capacity of including this information into the probability model
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