BOWL: Bayesian Optimization for Weight Learning in Probabilistic Soft Logic


  • Sriram Srinivasan University of California Santa Cruz
  • Golnoosh Farnadi Mila, Université de Montréal
  • Lise Getoor University of California Santa Cruz



Probabilistic soft logic (PSL) is a statistical relational learning framework that represents complex relational models with weighted first-order logical rules. The weights of the rules in PSL indicate their importance in the model and influence the effectiveness of the model on a given task. Existing weight learning approaches often attempt to learn a set of weights that maximizes some function of data likelihood. However, this does not always translate to optimal performance on a desired domain metric, such as accuracy or F1 score. In this paper, we introduce a new weight learning approach called Bayesian optimization for weight learning (BOWL) based on Gaussian process regression that directly optimizes weights on a chosen domain performance metric. The key to the success of our approach is a novel projection that captures the semantic distance between the possible weight configurations. Our experimental results show that our proposed approach outperforms likelihood-based approaches and yields up to a 10% improvement across a variety of performance metrics. Further, we performed experiments to measure the scalability and robustness of our approach on various realworld datasets.




How to Cite

Srinivasan, S., Farnadi, G., & Getoor, L. (2020). BOWL: Bayesian Optimization for Weight Learning in Probabilistic Soft Logic. Proceedings of the AAAI Conference on Artificial Intelligence, 34(06), 10267-10275.



AAAI Technical Track: Reasoning under Uncertainty