Learning probabilistic models of link structure
The Journal of Machine Learning Research
Relational Dependency Networks
The Journal of Machine Learning Research
Markov logic: a unifying language for knowledge and information management
Proceedings of the 17th ACM conference on Information and knowledge management
Granular modeling of web documents: impact on information retrieval systems
Proceedings of the 10th ACM workshop on Web information and data management
Extracting content structure for web pages based on visual representation
APWeb'03 Proceedings of the 5th Asia-Pacific web conference on Web technologies and applications
Loglinear models for first-order probabilistic reasoning
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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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.