Learning Distributional Programs for Relational Autocompletion
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Relational autocompletion is the problem of automatically filling out some missing fields in a relational database. 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 Dreaml – an approach to learn both the structure and the parameters of DC programs from databases that may contain missing information. To realize this, Dreaml integrates statistical modeling, distributional clauses with rule learning. The distinguishing features of Dreaml are that it 1) tackles relational autocompletion, 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.
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