Abstract
A key feature of ProPPR, a recent probabilistic logic language inspired by stochastic logic programs (SLPs), is its use of personalized PageRank for efficient inference. We adopt this view of probabilistic inference as a random walk over a graph constructed from a labeled logic program to investigate the relationship between these two languages, showing that the differences in semantics rule out direct, generally applicable translations between them.
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Acknowledgements
Dries Van Daele is supported by PF/10/010 (NATAR). A. Kimmig is supported by the Research Foundation Flanders (FWO).
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Van Daele, D., Kimmig, A., De Raedt, L. (2015). PageRank, ProPPR, and Stochastic Logic Programs. In: Davis, J., Ramon, J. (eds) Inductive Logic Programming. Lecture Notes in Computer Science(), vol 9046. Springer, Cham. https://doi.org/10.1007/978-3-319-23708-4_12
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DOI: https://doi.org/10.1007/978-3-319-23708-4_12
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