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.
Keywords
- Knowledge Base
- Maximum Entropy
- Transformation Rule
- Probabilistic Logic
- Predicate Symbol
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.
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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DOI: https://doi.org/10.1007/978-3-642-28472-4_13
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