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Comparing and Evaluating Approaches to Probabilistic Reasoning: Theory, Implementation, and Applications

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Transactions on Large-Scale Data- and Knowledge-Centered Systems VI

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 7600))

Abstract

The handling of uncertain information is of crucial importance for the success of expert systems. This paper gives an overview on logic-based approaches to probabilistic reasoning and goes into more details about recent developments for relational, respectively first-order, probabilistic methods like Markov logic networks, and Bayesian logic programs. In particular, we feature the maximum entropy approach as a powerful and elegant method that combines convenience with respect to knowledge representation with excellent inference properties. While comparing the different approaches is a difficult task due to the variety of the available concepts and to the absence of a common interface, we address this problem from both a conceptual and practical point of view. On a conceptual layer we propose and discuss several criteria by which first-order probabilistic methods can be distinguished, and apply these criteria to a series of approaches. On the practical layer, we briefly describe some systems for probabilistic reasoning, and go into more details on the KReator system as a versatile toolbox for various approaches to first-order probabilistic relational learning, modelling, and reasoning. Moreover, we illustrate applications of probabilistic logics in various scenarios.

The research reported here was partially supported by the Deutsche Forschungsgemeinschaft (grants KE 1413/2-2 and BE 1700/7-2).

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, TU Dortmund, 44221, Dortmund, Germany

    Gabriele Kern-Isberner

  2. Dept. of Computer Science, FernUniversität in Hagen, 58084, Hagen, Germany

    Christoph Beierle & Marc Finthammer

  3. Dept. of Computer Science, Universität Koblenz, 56070, Koblenz, Germany

    Matthias Thimm

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  1. Gabriele Kern-Isberner
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  2. Christoph Beierle
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  3. Marc Finthammer
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  4. Matthias Thimm
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Editors and Affiliations

  1. Institut de Recherche en Informatique de Toulouse (IRIT), Paul Sabatier University, 118 route de Narbonne, 31062, Toulouse Cedex, France

    Abdelkader Hameurlain

  2. FAW - Institute for Applied Knowledge Processing, University of Linz, Altenbergerstraße 69, 4040, Linz, Austria

    Josef Küng

  3. FAW, University of Linz, Altenbergerstraße 69, 4040, Linz, Austria

    Roland Wagner

  4. Marriott School, Brigham Young University, 784 TNRB, 84602, Provo, UT, USA

    Stephen W. Liddle

  5. Software Competence Center Hagenberg, Softwarepark 21, 4232, Hagenberg, Austria

    Klaus-Dieter Schewe

  6. School of Information Technology and Electrical Engineering, University of Queensland, 4072, Brisbane, QLD, Australia

    Xiaofang Zhou

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Kern-Isberner, G., Beierle, C., Finthammer, M., Thimm, M. (2012). Comparing and Evaluating Approaches to Probabilistic Reasoning: Theory, Implementation, and Applications. In: Hameurlain, A., Küng, J., Wagner, R., Liddle, S.W., Schewe, KD., Zhou, X. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems VI. Lecture Notes in Computer Science, vol 7600. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34179-3_2

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