Probabilistic Relational Models with Relational Uncertainty: An Early Study in Web Page Classification

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.

[1]  Daphne Koller,et al.  Probabilistic Relational Models , 1999, ILP.

[2]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[3]  Pedro M. Domingos 1 Markov Logic: A Unifying Framework for Statistical Relational Learning , 2010 .

[4]  Jennifer Neville,et al.  Dependency networks for relational data , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[5]  Andrew McCallum,et al.  A comparison of event models for naive bayes text classification , 1998, AAAI 1998.

[6]  Ben Taskar,et al.  Learning Probabilistic Models of Relational Structure , 2001, ICML.

[7]  Lise Getoor,et al.  Learning Probabilistic Relational Models with Structural Uncertainty , 2000 .

[8]  Ben Taskar,et al.  Learning Probabilistic Models of Link Structure , 2003, J. Mach. Learn. Res..

[9]  Ben Taskar,et al.  Markov Logic: A Unifying Framework for Statistical Relational Learning , 2007 .

[10]  Francesco Archetti,et al.  Granular modeling of web documents: impact on information retrieval systems , 2008, WIDM '08.

[11]  Wei-Ying Ma,et al.  Extracting Content Structure for Web Pages Based on Visual Representation , 2003, APWeb.

[12]  Jennifer Neville,et al.  Relational Dependency Networks , 2007, J. Mach. Learn. Res..

[13]  Manfred Jaeger,et al.  Relational Bayesian Networks , 1997, UAI.

[14]  Ben Taskar,et al.  Probabilistic Models of Text and Link Structure for Hypertext Classification , 2001 .

[15]  James Cussens Loglinear models for first-order probabilistic reasoning , 1999, UAI.

[16]  Lise Getoor,et al.  Learning Probabilistic Relational Models , 1999, IJCAI.

[17]  Luc De Raedt,et al.  Basic Principles of Learning Bayesian Logic Programs , 2008, Probabilistic Inductive Logic Programming.

[18]  Jennifer Neville,et al.  Learning relational probability trees , 2003, KDD '03.

[19]  Pedro M. Domingos Markov logic: a unifying language for knowledge and information management , 2008, CIKM '08.