DocumentCode :
1841317
Title :
Probabilistic Relational Models with Relational Uncertainty: An Early Study in Web Page Classification
Author :
Fersini, E. ; Messina, E. ; Archetti, F.
Volume :
3
fYear :
2009
fDate :
15-18 Sept. 2009
Firstpage :
139
Lastpage :
142
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.
Keywords :
Bayesian methods; Capacity planning; Conferences; Informatics; Intelligent agent; Logic; Measurement uncertainty; Probability; Skeleton; Web pages; Keywords-Probabilistic Relational Models; Relational Uncertainty; Web Page Classification;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Web Intelligence and Intelligent Agent Technologies, 2009. WI-IAT '09. IEEE/WIC/ACM International Joint Conferences on
Conference_Location :
Milan, Italy
Print_ISBN :
978-0-7695-3801-3
Electronic_ISBN :
978-1-4244-5331-3
Type :
conf
DOI :
10.1109/WI-IAT.2009.249
Filename :
5284946
Link To Document :
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