Learning Distributional Programs for Relational Autocompletion. Issue 1 (26th January 2022)

• Record Type: Journal Article
• Authors: KUMAR, NITESH; KUŽELKA, ONDŘEJ; DE RAEDT, LUC
• Is Part Of: Theory and practice of logic programming. Volume 22:Issue 1(2022)
• Page Start: 81
• Page End: 114
• Publication Date: 2022-01-26
• Subjects: probabilistic logic programming -- statistical relational learning -- structure learning -- inductive logic programming; Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S1471068421000144
• Languages: English
• ISSNs: 1471-0684

Abstract

Abstract: 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 (DCs), 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 DCs with rule learning. The distinguishing features of DiceML are that it (1) tackles autocompletion in relational data, (2) learns DCs extended with statistical models, (3) deals with both discrete and continuous distributions, (4) can exploit background knowledge, and (5) uses an expectation–maximization-based (EM) algorithm to cope with missing data. The empirical results show the promise of the approach, even when there is missing data.