Relational data factorization View Full Text


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Article Info

DATE

2017-12

AUTHORS

Sergey Paramonov, Matthijs van Leeuwen, Luc De Raedt

ABSTRACT

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. More... »

PAGES

1867-1904

References to SciGraph publications

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  • 2012. Mining Chains of Relations in DATA MINING: FOUNDATIONS AND INTELLIGENT PARADIGMS
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  • 2011. Itemset Mining as a Challenge Application for Answer Set Enumeration in LOGIC PROGRAMMING AND NONMONOTONIC REASONING
  • 2016. An Exercise in Declarative Modeling for Relational Query Mining in INDUCTIVE LOGIC PROGRAMMING
  • 2004. Tiling Databases in DISCOVERY SCIENCE
  • 2002. Abduction in Logic Programming in COMPUTATIONAL LOGIC: LOGIC PROGRAMMING AND BEYOND
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  • 2006. Pattern Teams in KNOWLEDGE DISCOVERY IN DATABASES: PKDD 2006
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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10994-017-5660-6

    DOI

    http://dx.doi.org/10.1007/s10994-017-5660-6

    DIMENSIONS

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    43 schema:description 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.
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