论文标题
学习分配计划用于关系自动完成
Learning Distributional Programs for Relational Autocompletion
论文作者
论文摘要
关系自动完成是在多关系数据中自动填充一些丢失值的问题。我们在分布条款（DC）的概率逻辑编程框架内解决了此问题，该框架支持离散和连续概率分布。在此框架内，我们介绍了DICEML {从关系数据（可能缺少数据）中学习DC程序的结构和参数的方法。为了实现这一目标，DICEML将统计建模和分配条款与规则学习相结合。 DICEML的区别特征是1）处理关系数据中的自动完成，2）学习使用统计模型扩展的分布条款，3）处理离散和连续分布，4）可以利用背景知识，5）使用一种预期 - 磁性算法基于预期的算法，以应对缺少数据。经验结果表明了该方法的希望，即使缺少数据。
Relational autocompletion is the problem of automatically filling out some missing values in multi-relational data. We tackle this problem within the probabilistic logic programming framework of Distributional Clauses (DC), which supports both discrete and continuous probability distributions. Within this framework, we introduce DiceML { an approach to learn both the structure and the parameters of DC programs from relational data (with possibly missing data). To realize this, DiceML integrates statistical modeling and distributional clauses with rule learning. The distinguishing features of DiceML are that it 1) tackles autocompletion in relational data, 2) learns distributional clauses extended with statistical models, 3) deals with both discrete and continuous distributions, 4) can exploit background knowledge, and 5) uses an expectation-maximization based algorithm to cope with missing data. The empirical results show the promise of the approach, even when there is missing data.
