Learning Distributional Programs for Relational Autocompletion
This addresses relational autocompletion for applications handling incomplete relational datasets, but it appears incremental as it builds on existing probabilistic logic programming frameworks.
The paper tackled the problem of automatically filling missing values in multi-relational data by introducing DiceML, an approach that learns both structure and parameters of Distributional Clauses programs from relational data, showing promise in empirical results even with missing data.
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.