  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.
