Evaluation and Comparison Criteria for Approaches to Probabilistic Relational Knowledge Representation

  • Christoph Beierle
  • Marc Finthammer
  • Gabriele Kern-Isberner
  • Matthias Thimm
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7006)


In the past ten years, the areas of probabilistic inductive logic programming and statistical relational learning put forth a large collection of approaches to combine relational representations of knowledge with probabilistic reasoning. Here, we develop a series of evaluation and comparison criteria for those approaches and focus on the point of view of knowledge representation and reasoning. These criteria address abstract demands such as language aspects, the relationships to propositional probabilistic and first-order logic, and their treatment of information on individuals. We discuss and illustrate the criteria thoroughly by applying them to several approaches to probabilistic relational knowledge representation, in particular, Bayesian logic programs, Markov logic networks, and three approaches based on the principle of maximum entropy.


Bayesian Network Maximum Entropy Knowledge Representation Propositional Knowledge Ground Instance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Christoph Beierle
    • 1
  • Marc Finthammer
    • 1
  • Gabriele Kern-Isberner
    • 2
  • Matthias Thimm
    • 2
  1. 1.Dept. of Computer ScienceFernUniversitätHagenGermany
  2. 2.Dept. of Computer ScienceTU DortmundDortmundGermany

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