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Analyzing Web Profiles using Probabilistic Ontologies

Kozak, Pawel and Tolle, Karsten (2011) Analyzing Web Profiles using Probabilistic Ontologies. pp. 1-4. In: Proceedings of the ACM WebSci'11, June 14-17 2011, Koblenz, Germany.

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Official URL: http://www.websci11.org/fileadmin/websci/Posters/23_paper.pdf

Abstract

In this paper, we discuss our probabilistic ontological solution for analysis of Web profiles. The analysis of Web profiles is a very demanding and multi-layered task; especially probabilistic information in terms of probability distributions and weights is often the key to an expressive analysis. In our research we designed the Probabilistic Profile Analysis Ontology (PPAO) using Markov Logic Networks (MLNs) afterwards we conducted experimentation to evaluate the scalability and expressiveness of this solution. MLNs were chosen as the underlying formalism because of their probabilistic nature, intuitive and expressive modeling ability due to first order logic as well as ability to use approximation algorithms to improve reasoning performance. A similarity benchmark between profiles was designed within the PPAO concept as a special task and real static and probabilistic data from the German IRCLOVE community www.irclove.de was used for the analysis.

Item Type:Conference or Workshop Item (Poster)
Web Science Comments:WebSci Conference 2011
Subjects:WS2 Artificial Intelligence
WS2 Artificial Intelligence > WS21 Knowledge Representation
Web Science Events > Web Science 2011
ID Code:519
Deposited By: Lisa Sugiura
Deposited On:07 Jun 2011 16:38
Last Modified:25 Oct 2011 17:11

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