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.

中文翻译：

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学习关系自动完成的分布式程序

关系自动补全是自动填充多关系数据中一些缺失值的问题。我们在分布子句 (DC) 的概率逻辑编程框架内解决了这个问题，该框架支持离散和连续概率分布。在这个框架内，我们引入了 DiceML——一种从关系数据（可能有缺失数据）中学习 DC 程序的结构和参数的方法。为了实现这一点，DiceML 将统计建模和分布子句与规则学习相结合。DiceML 的显着特点是它 1) 处理关系数据中的自动完成，2) 学习用统计模型扩展的分布子句，3) 处理离散和连续分布，4) 可以利用背景知识，5) 使用基于期望最大化的算法来处理缺失数据。实证结果显示了该方法的前景，即使存在缺失数据也是如此。