Learning distributional programs for relational autocompletion

Kumar, Nitesh ORCID: https://orcid.org/0000-0002-9301-3876, Kuželka, Ondřej and De Raedt, Luc 2022. Learning distributional programs for relational autocompletion. Theory and Practice of Logic Programming 22 (1) , pp. 81-114. 10.1017/S1471068421000144

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Cambridge University Press
ISSN:	1471-0684
Date of First Compliant Deposit:	21 October 2024
Last Modified:	10 Dec 2024 16:30
URI:	https://orca.cardiff.ac.uk/id/eprint/173173

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