Machine Learning

, Volume 106, Issue 12, pp 1867–1904 | Cite as

Relational data factorization

  • Sergey Paramonov
  • Matthijs van Leeuwen
  • Luc De Raedt


Motivated by an analogy with matrix factorization, we introduce the problem of factorizing relational data. In matrix factorization, one is given a matrix and has to factorize it as a product of other matrices. In relational data factorization, the task is to factorize a given relation as a conjunctive query over other relations, i.e., as a combination of natural join operations. Given a conjunctive query and the input relation, the problem is to compute the extensions of the relations used in the query. Thus, relational data factorization is a relational analog of matrix factorization; it is also a form of inverse querying as one has to compute the relations in the query from the result of the query. The result of relational data factorization is neither necessarily unique nor required to be a lossless decomposition of the original relation. Therefore, constraints can be imposed on the desired factorization and a scoring function is used to determine its quality (often similarity to the original data). Relational data factorization is thus a constraint satisfaction and optimization problem. We show how answer set programming can be used for solving relational data factorization problems.


Answer set programming Inductive logic programming Pattern mining Relational data Factorization Data mining Declarative modeling 



We would like to thank Marc Denecker, Tias Guns, Benjamin Negrevergne, Siegfried Nijssen, and Behrouz Babaki for their help and assistance, and last but not least the ICON project (FP7-ICT-2011-C) and FWO for funding this work.


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Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.Machine Learning, Department of Computer ScienceKU LeuvenLeuvenBelgium
  2. 2.Leiden Institute of Advanced Computer ScienceLeiden UniversityLeidenThe Netherlands

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