Statistical Relational Learning Group
BOWL: Bayesian Optimization for Weight Learning in Probabilistic Soft Logic
| Title | BOWL: Bayesian Optimization for Weight Learning in Probabilistic Soft Logic |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Srinivasan, S, Farnadi, G, Getoor, L |
| Conference Name | AAAI Conference on Artificial Intelligence (AAAI) |
| Abstract | 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 real world datasets. |
