Abstract
In this paper we propose a web document classification approach based on an extended version of Probabilistic Relational Models (PRMs). In particular PRMs have been augmented in order to include uncertainty over relationships, represented by hyperlinks. Our extension, called PRM with Relational Uncertainty, has been evaluated on real data for web document classification purposes. Experimental results shown the potentiality of the proposed model of capturing the real semantic relevance of hyperlinks and the capacity of embedding this information in the classification process.
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Fersini, E., Messina, E., Archetti, F. (2010). Web Page Classification: A Probabilistic Model with Relational Uncertainty. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Computational Intelligence for Knowledge-Based Systems Design. IPMU 2010. Lecture Notes in Computer Science(), vol 6178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14049-5_12
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DOI: https://doi.org/10.1007/978-3-642-14049-5_12
Publisher Name: Springer, Berlin, Heidelberg
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