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Relational autocompletion is the problem of automatically filling out somemissing values in multi-relational data. We tackle this problem within theprobabilistic logic programming framework of Distributional Clauses (DC), whichsupports both discrete and continuous probability distributions. Within thisframework, we introduce DiceML { an approach to learn both the structure andthe parameters of DC programs from relational data (with possibly missingdata). To realize this, DiceML integrates statistical modeling anddistributional clauses with rule learning. The distinguishing features ofDiceML are that it 1) tackles autocompletion in relational data, 2) learnsdistributional clauses extended with statistical models, 3) deals with bothdiscrete and continuous distributions, 4) can exploit background knowledge, and5) uses an expectation-maximization based algorithm to cope with missing data.The empirical results show the promise of the approach, even when there ismissing data.

Learning Distributional Programs for Relational Autocompletion