In the last decade, new approaches focused on modeling uncertainty over complex relational data have been developed. In this paper, one of the most promising of such approaches, known as probabilistic relational model (PRM), has been investigated and extended in order to measure and include semantic relationships for addressing web page classification problems. Experimental results show the potential of the proposed method of capturing the "strength" of existing relationships (links) and the capacity of including this information into the probability model. © 2013 World Scientific Publishing Company.

Fersini, E., Messina, V. (2013). Web page classification through probabilistic relational models. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 27(4), 831-854 [10.1142/S0218001413500134].

Web page classification through probabilistic relational models

Fersini, E;Messina, V.
2013

Abstract

In the last decade, new approaches focused on modeling uncertainty over complex relational data have been developed. In this paper, one of the most promising of such approaches, known as probabilistic relational model (PRM), has been investigated and extended in order to measure and include semantic relationships for addressing web page classification problems. Experimental results show the potential of the proposed method of capturing the "strength" of existing relationships (links) and the capacity of including this information into the probability model. © 2013 World Scientific Publishing Company.
Articolo in rivista - Articolo scientifico
Web document classi ̄cation; probabilistic relational models
English
2013
27
4
831
854
1350013
none
Fersini, E., Messina, V. (2013). Web page classification through probabilistic relational models. INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 27(4), 831-854 [10.1142/S0218001413500134].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/45506
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