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Learning Distributional Programs for Relational Autocompletion

Published online by Cambridge University Press:  26 August 2021

NITESH KUMAR Open the ORCID record for NITESH KUMAR [Opens in a new window] ,
ONDŘEJ KUŽELKA  and
LUC DE RAEDT
NITESH KUMAR
Affiliation:
Department of Computer Science, KU Leuven, Belgium
ONDŘEJ KUŽELKA
Affiliation:
Department of Computer Science, Czech Technical University in Prague, Czechia
LUC DE RAEDT
Affiliation:
Department of Computer Science, KU Leuven, Belgium
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Abstract

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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.

Keywords

probabilistic logic programmingstatistical relational learningstructure learninginductive logic programming

Information

Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 22 , Issue 1 , January 2022 , pp. 81 - 114
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Footnotes

*

This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No [694980] SYNTH: Synthesising Inductive Data Models) and the Flemish Government under the “Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” program. OK’s work has been supported by the OP VVV project CZ.02.1.01/0.0/0.0/16_019/0000765 “Research Center for Informatics”, by the Czech Science Foundation project “Generative Relational Models” (20-19104Y) and a donation from X-Order Lab. Part of this work was done while OK was with KU Leuven and was supported by Research Foundation – Flanders (project G.0428.15).

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