TensorLog: a probabilistic database implemented using deep-learning infrastructure. We present an implementation of a probabilistic first-order logic called TensorLog, in which classes of logical queries are compiled into differentiable functions in a neural-network infrastructure such as Tensorflow or Theano. This leads to a close integration of probabilistic logical reasoning with deep-learning infrastructure: in particular, it enables high-performance deep learning frameworks to be used for tuning the parameters of a probabilistic logic. The integration with these frameworks enables use of GPU-based parallel processors for inference and learning, making TensorLog the first highly parallellizable probabilistic logic. Experimental results show that TensorLog scales to problems involving hundreds of thousands of knowledge-base triples and tens of thousands of examples.
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References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Badreddine, Samy; d’Avila Garcez, Artur; Serafini, Luciano; Spranger, Michael: Logic tensor networks (2022)
- Manhaeve, Robin; Dumančić, Sebastijan; Kimmig, Angelika; Demeester, Thomas; De Raedt, Luc: Neural probabilistic logic programming in DeepProbLog (2021)
- Šourek, Gustav; Železný, Filip; Kuželka, Ondřej: Beyond graph neural networks with lifted relational neural networks (2021)
- Cohen, William; Yang, Fan; Mazaitis, Kathryn Rivard: TensorLog: a probabilistic database implemented using deep-learning infrastructure (2020)
- Quoc, Tuan Nguyen; Inoue, Katsumi; Sakama, Chiaki: Enhancing linear algebraic computation of logic programs using sparse representation (2020)
- Yang, Zhun: Extending answer set programs with neural networks (2020)