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Relational data factorization
Machine Learning, Department of Computer Science, KU Leuven, Leuven, Belgium.
Machine Learning, Department of Computer Science, KU Leuven, Leuven, Belgium; Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands.
Machine Learning, Department of Computer Science, KU Leuven, Leuven, Belgium.ORCID iD: 0000-0002-6860-6303
2017 (English)In: Machine Learning, ISSN 0885-6125, E-ISSN 1573-0565, Vol. 106, no 12, p. 1867-1904Article in journal (Refereed) Published
Abstract [en]

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 (ReDF), 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 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.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 106, no 12, p. 1867-1904
Keywords [en]
Answer set programming, Inductive logic programming, Pattern mining, Relational data, Factorization, Data mining, Declarative modeling
National Category
Mechanical Engineering Computer Engineering
Identifiers
URN: urn:nbn:se:oru:diva-84427DOI: 10.1007/s10994-017-5660-6ISI: 000415881500002Scopus ID: 2-s2.0-85027078830OAI: oai:DiVA.org:oru-84427DiVA, id: diva2:1452323
Conference
25th International Conference on Inductive Logic Programming (ILP), Kyoto Univ, Kyoto, Japan, August, 20-22, 2015.
Available from: 2020-07-06 Created: 2020-07-06 Last updated: 2020-08-24Bibliographically approved

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De Raedt, Luc

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