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International Symposium on Methodologies for Intelligent Systems

ISMIS 2011: Foundations of Intelligent Systems pp 250–259Cite as

Learning with Semantic Kernels for Clausal Knowledge Bases

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6804))

Abstract

Many applicative domains require complex multi-relational representations. We propose a family of kernels for relational representations to produce statistical classifiers that can be effectively employed in a variety of such tasks. The kernel functions are defined over the set of objects in a knowledge base parameterized on a notion of context, represented by a committee of concepts expressed through logic clauses. A preliminary feature construction phase based on genetic programming allows for the selection of optimized contexts. An experimental session on the task of similarity search proves the practical effectiveness of the method.

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Author information

Authors and Affiliations

  1. Computer Science Department, University of Bari, Italy

    Nicola Fanizzi & Claudia d’Amato

Authors
  1. Nicola Fanizzi
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  2. Claudia d’Amato
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Editor information

Editors and Affiliations

  1. Faculty of Electronics and Information Technology, Institute of Computer Science, Warsaw University of Technology,, Nowowiejska 15/19, 00-665, Warsaw, Poland

    Marzena Kryszkiewicz

  2. Faculty of Electronics and Information Technology, Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19, 00-665, Warsaw, Poland

    Henryk Rybinski

  3. University of Warsaw, 02-097, Warsaw, Poland

    Andrzej Skowron

  4. Faculty of Electronics and Information Technology, Institute of Computer Science, Warsaw University of Technology, Nowowiejska 15/19,, 00-665, Warsaw, Poland

    Zbigniew W. Raś

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© 2011 Springer-Verlag Berlin Heidelberg

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Fanizzi, N., d’Amato, C. (2011). Learning with Semantic Kernels for Clausal Knowledge Bases. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2011. Lecture Notes in Computer Science(), vol 6804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21916-0_28

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  • DOI: https://doi.org/10.1007/978-3-642-21916-0_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21915-3

  • Online ISBN: 978-3-642-21916-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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