Kernels over Relational Algebra Structures

  • Adam Woźnica
  • Alexandros Kalousis
  • Melanie Hilario
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3518)


In this paper we present a novel and general framework based on concepts of relational algebra for kernel-based learning over relational schema. We exploit the notion of foreign keys to define a new attribute that we call instance-set and we use this type of attribute to define a tree like structured representation of the learning instances. We define kernel functions over relational schemata which are instances of \(\Re\)-Convolution kernels and use them as a basis for a relational instance-based learning algorithm. These kernels can be considered as being defined over typed and unordered trees where elementary kernels are used to compute the graded similarity between nodes. We investigate their formal properties and evaluate the performance of the relational instance-based algorithm on a number of relational data sets.


Feature Space Relational Schema Relational Algebra Polynomial Kernel Convolution Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Adam Woźnica
    • 1
  • Alexandros Kalousis
    • 1
  • Melanie Hilario
    • 1
  1. 1.Computer Science DepartmentUniversity of GenevaGeneva 4Switzerland

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