Abstract
When extending probabilistic logic to a relational setting, it is desirable to still be able to use efficient inference mechanisms developed for the propositional case. In this paper, we investigate the relational probabilistic conditional logic FO-PCL whose semantics employs the principle of maximum entropy. While in general, this semantics is defined via the ground instances of the rules in an FO-PCL knowledge base \(\mathcal{R}\), the maximum entropy model can be computed on the level of rules rather than on the level of instances of the rules if \(\mathcal{R}\) is parametrically uniform, thus providing lifted inference.We elaborate in detail the reasons precluding \(\mathcal{R}\) from being parametrically uniform. Based on this investigation, we derive a new syntactic criterion for parametric uniformity and develop an algorithm that transforms any FO-PCL knowledge base \(\mathcal{R}\) into an equivalent knowledge base \(\mathcal{R'}\) that is parametrically uniform.
The research reported here was partially supported by the DFG - Deutsche Forschungsgemeinschaft (grant BE 1700/7-2).
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Krämer, A., Beierle, C. (2012). On Lifted Inference for a Relational Probabilistic Conditional Logic with Maximum Entropy Semantics. In: Lukasiewicz, T., Sali, A. (eds) Foundations of Information and Knowledge Systems. FoIKS 2012. Lecture Notes in Computer Science, vol 7153. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28472-4_13
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