Encoding Markov logic networks in Possibilistic Logic

Ondrej Kuzelka Cardiff University, Jesse Davis KU Leuven, Steven Schockaert Cardiff University
Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:536-545, 2015.

Abstract

Markov logic uses weighted formulas to compactly encode a probability distribution over possible worlds. Despite the use of logical formulas, Markov logic networks (MLNs) can be difficult to interpret, due to the often counter-intuitive meaning of their weights. To address this issue, we propose a method to construct a possibilistic logic theory that exactly captures what can be derived from a given MLN using maximum a posteriori (MAP) inference. Unfortunately, the size of this theory is exponential in general. We therefore also propose two methods which can derive compact theories that still capture MAP inference, but only for specific types of evidence. These theories can be used, among others, to make explicit the hidden assumptions underlying an MLN or to explain the predictions it makes.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR13-university15k, title = {Encoding {M}arkov logic networks in Possibilistic Logic}, author = {University, Ondrej Kuzelka Cardiff and Leuven, Jesse Davis KU and University, Steven Schockaert Cardiff}, booktitle = {Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence}, pages = {536--545}, year = {2015}, editor = {Meila, Marina and Heskes, Tom}, volume = {R13}, series = {Proceedings of Machine Learning Research}, month = {12--16 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r13/main/assets/university15k/university15k.pdf}, url = {https://proceedings.mlr.press/r13/university15k.html}, abstract = {Markov logic uses weighted formulas to compactly encode a probability distribution over possible worlds. Despite the use of logical formulas, Markov logic networks (MLNs) can be difficult to interpret, due to the often counter-intuitive meaning of their weights. To address this issue, we propose a method to construct a possibilistic logic theory that exactly captures what can be derived from a given MLN using maximum a posteriori (MAP) inference. Unfortunately, the size of this theory is exponential in general. We therefore also propose two methods which can derive compact theories that still capture MAP inference, but only for specific types of evidence. These theories can be used, among others, to make explicit the hidden assumptions underlying an MLN or to explain the predictions it makes.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Encoding Markov logic networks in Possibilistic Logic %A Ondrej Kuzelka Cardiff University %A Jesse Davis KU Leuven %A Steven Schockaert Cardiff University %B Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2015 %E Marina Meila %E Tom Heskes %F pmlr-vR13-university15k %I PMLR %P 536--545 %U https://proceedings.mlr.press/r13/university15k.html %V R13 %X Markov logic uses weighted formulas to compactly encode a probability distribution over possible worlds. Despite the use of logical formulas, Markov logic networks (MLNs) can be difficult to interpret, due to the often counter-intuitive meaning of their weights. To address this issue, we propose a method to construct a possibilistic logic theory that exactly captures what can be derived from a given MLN using maximum a posteriori (MAP) inference. Unfortunately, the size of this theory is exponential in general. We therefore also propose two methods which can derive compact theories that still capture MAP inference, but only for specific types of evidence. These theories can be used, among others, to make explicit the hidden assumptions underlying an MLN or to explain the predictions it makes. %Z Reissued by PMLR on 04 October 2026.
APA
University, O.K.C., Leuven, J.D.K. & University, S.S.C.. (2015). Encoding Markov logic networks in Possibilistic Logic. Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R13:536-545 Available from https://proceedings.mlr.press/r13/university15k.html. Reissued by PMLR on 04 October 2026.

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