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Erschienen in:
2012 | OriginalPaper | Buchkapitel
On Lifted Inference for a Relational Probabilistic Conditional Logic with Maximum Entropy Semantics
verfasst von : Annika Krämer, Christoph Beierle
Erschienen in: Foundations of Information and Knowledge Systems
Verlag: Springer Berlin Heidelberg
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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.