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    |    o-o o--o    o-o  oo |     | O |  oo o-o OO   o-o o   o
    |    | | |  | | |    | | |     |   | | | |   | \  | |  \ /
    O---oo-o o--O |  o-o o-o-o     o   o o-o-o   o  o o-o   o
                |
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    o--o              o               o--o       o    o
    |   |             |               |    o     |    |
    O-Oo   oo o-o   o-O o-o o-O-o     O-o    o-o |  o-O o-o
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    o   o o-o-o  o  o-o o-o o o o     o    | o-o o  o-o o-o

    Logical Markov Random Fields.

LoMRF: Logical Markov Random Fields

LoMRF is an open-source implementation of Markov Logic Networks (MLNs) written in Scala programming language.

Features overview:

1. Parallel grounding algorithm based on Akka Actors library.
2. Marginal (MC-SAT) and MAP (MaxWalkSAT and LP-relaxed Integer Linear Programming) inference (lomrf infer).
3. Batch and on-line Weight Learning (Max-Margin, AdaGrad and CDA) (lomrf wlearn).
4. On-line Structure Learning (OSL and OSLa) (lomrf slearn).
5. MLN knowledge base compilation (lomrf compile):

• Predicate completion.
• Clausal form transformation.
• Replacement of functions with utility predicates and vice versa.
• Reads and produces Alchemy compatible MLN files.

6. Can export ground MRF in various formats (lomrf export).
7. Can compare MLN theories (lomrf diff).
8. Online supervision completion on semi-supervised training sets [currently experimental] (lomrf supervision)

Documentation

Latest documentation.

Contributions

Contributions are welcome, for details see CONTRIBUTING.md.

License

Copyright (c) 2014 - 2019 Anastasios Skarlatidis and Evangelos Michelioudakis

LoMRF is licensed under the Apache License, Version 2.0: https://www.apache.org/licenses/LICENSE-2.0

Reference in Scientific Publications

Please use the following BibTex entry when you cite LoMRF in your papers:

@misc{LoMRF,
	author = {Anastasios Skarlatidis and Evangelos Michelioudakis},
	title = {{Logical Markov Random Fields (LoMRF): an open-source implementation of Markov Logic Networks}},
	url = {https://github.com/anskarl/LoMRF},
	year = {2014}
}