BOWL: Bayesian Optimization for Weight Learning in Probabilistic Soft Logic
BOWL: Bayesian Optimization for Weight Learning in Probabilistic Soft Logic
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DOI：
10.1609/aaai.v34i06.6589
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发表时间：
2020-04
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影响因子：
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通讯作者：
S. Srinivasan;G. Farnadi;L. Getoor
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文献类型：
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作者：
S. Srinivasan;G. Farnadi;L. Getoor
概率软逻辑（ProbabilitySoftLogic，PSL）是一个统计关系学习框架，它用加权一阶逻辑规则表示复杂的关系模型。PSL中规则的权重表明它们在模型中的重要性，并影响模型对给定任务的有效性。现有的权重学习方法通常试图学习一组最大化数据可能性的函数的权重。然而，这并不总是转化为所需域度量的最佳性能，例如准确性或F1分数。在本文中，我们介绍了一种新的权重学习方法，称为贝叶斯优化的权重学习（BOWL）的基础上高斯过程回归，直接优化权重上选择的域性能指标。我们的方法成功的关键是一个新的投影，捕捉可能的权重配置之间的语义距离。我们的实验结果表明，我们提出的方法优于基于可能性的方法，并在各种性能指标上提高了10%。此外，我们进行了实验，以衡量我们的方法在各种现实世界的数据集上的可扩展性和鲁棒性。
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.