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