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Towards Mining Generalized Patterns from RDF Data and a Domain Ontology

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Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1524))

Abstract

Nowadays, linked data (LD) are ubiquitous and mining them for knowledge, e.g. frequent patterns, needs not be argued for.

A domain ontology (DO) on top of a LD dataset enables the discovery of abstract patterns, a.k.a. generalized, capturing –rather than identical sub-structures–conceptual regularities in data. Yet with the resulting ontologically-generalized graph patterns (OGP), a miner faces the combined challenges of graph topology and a label hierarchy, which amplifies well-known difficulties with graphs such as support counting or non redundant pattern listing. As OGP mining is yet to be addressed in its generality, we propose a formalization and study two workaround methods that avoid tackling it head-on, i.e. deal with each aspect separately. Both perform pure graph mining with adapted label sets: gSpan-OF merely strips labels of hierarchical structure while Tax-ON first mines frequent graph topologies with only root classes as labels, then successively refines labels on each topology.

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Notes

  1. 1.

    Pattern labels in \({\mathcal L}_p\) will be prefixed by & to differentiate them from ontology entities.

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Author information

Authors and Affiliations

  1. CRIA, Département d’informatique, UQÀM, Montréal, Canada

    Tomas Martin, Victor Fuentes, Petko Valtchev, Abdoulaye Baniré Diallo & Mounir Boukadoum

  2. LACIM, Département d’informatique, UQÀM, Montréal, Canada

    Victor Fuentes, Abdoulaye Baniré Diallo & Maxime Leduc

  3. Lactanet, Sainte-Anne-de-Bellevue, Canada

    René Lacroix

Authors
  1. Tomas Martin
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  2. Victor Fuentes
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  3. Petko Valtchev
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  4. Abdoulaye Baniré Diallo
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  5. René Lacroix
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  6. Maxime Leduc
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  7. Mounir Boukadoum
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Corresponding author

Correspondence to Tomas Martin .

Editor information

Editors and Affiliations

  1. IKIM, Ruhr-University Bochum, Bochum, Germany

    Michael Kamp

  2. University of Sydney, Sydney, NSW, Australia

    Irena Koprinska

  3. University of Namur, Namur, Belgium

    Adrien Bibal

  4. University of Rennes 1, Rennes, France

    Tassadit Bouadi

  5. University of Namur, Namur, Belgium

    Benoît Frénay

  6. Inria, Rennes, France

    Luis Galárraga

  7. University of Antwerp, Antwerp, Belgium

    José Oramas

  8. Ruhr University Bochum, Bochum, Germany

    Linara Adilova

  9. Royal Holloway University of London, Egham, UK

    Yamuna Krishnamurthy

  10. Ghent University, Ghent, Belgium

    Bo Kang

  11. Université Jean Monnet, Saint-Etienne cedex 2, France

    Christine Largeron

  12. Ghent University, Gent, Belgium

    Jefrey Lijffijt

  13. Telecom Paris, Paris, France

    Tiphaine Viard

  14. University of Bonn, Bonn, Germany

    Pascal Welke

  15. Norwegian Univesity of Science and Technology, Trondheim, Norway

    Massimiliano Ruocco

  16. BI Norwegian Business School, Oslo, Norway

    Erlend Aune

  17. University of Pisa, Pisa, Italy

    Claudio Gallicchio

  18. University of Duisburg-Essen, Essen, Germany

    Gregor Schiele

  19. Graz University of Technology, Graz, Austria

    Franz Pernkopf

  20. Xilinx Research, Dublin, Ireland

    Michaela Blott

  21. Heidelberg University, Heidelberg, Germany

    Holger Fröning

  22. Heidelberg University, Heidelberg, Germany

    Günther Schindler

  23. University of Pisa, Pisa, Italy

    Riccardo Guidotti

  24. University of Pisa, Pisa, Italy

    Anna Monreale

  25. ISTI-CNR, Pisa, Italy

    Salvatore Rinzivillo

  26. Warsaw University of Technology, Warsaw, Poland

    Przemyslaw Biecek

  27. Freie Universität Berlin, Berlin, Germany

    Eirini Ntoutsi

  28. Eindhoven University of Technology, Eindhoven, The Netherlands

    Mykola Pechenizkiy

  29. Leibniz University Hannover, Hannover, Germany

    Bodo Rosenhahn

  30. University of Sussex, Brighton, UK

    Christopher Buckley

  31. University of Chieti-Pescara, Chieti, Italy

    Daniela Cialfi

  32. Radboud University Nijmegen, Nijmegen, The Netherlands

    Pablo Lanillos

  33. McGill University, Montreal, Canada

    Maxwell Ramstead

  34. Ghent University, Ghent, Belgium

    Tim Verbelen

  35. University of Lisbon, Lisboa, Portugal

    Pedro M. Ferreira

  36. University of Bari Aldo Moro, Bari, Italy

    Giuseppina Andresini

  37. Universita di Bari Aldo Moro, Bari, Italy

    Donato Malerba

  38. University of Lisbon, Lisbon, Portugal

    Ibéria Medeiros

  39. Shenzhen University, Shenzhen, China

    Philippe Fournier-Viger

  40. Harbin Institute of Technology, Harbin, China

    M. Saqib Nawaz

  41. University of Córdoba, Córdoba, Spain

    Sebastian Ventura

  42. Peking University, Beijing, China

    Meng Sun

  43. Noah's Ark Lab, Huawei, Beijing, China

    Min Zhou

  44. UniCredit, Milan, Italy

    Valerio Bitetta

  45. UniCredit, Rome, Italy

    Ilaria Bordino

  46. UniCredit, Milan, Italy

    Andrea Ferretti

  47. Unicredit, Rome, Italy

    Francesco Gullo

  48. ENEA Headquarters, Portici, Italy

    Giovanni Ponti

  49. Unicredit, Rome, Italy

    Lorenzo Severini

  50. University of Porto, Porto, Portugal

    Rita Ribeiro

  51. University of Porto, Porto, Portugal

    João Gama

  52. UPC BarcelonaTech, Barcelona, Spain

    Ricard Gavaldà

  53. Northwestern University, Chicago, IL, USA

    Lee Cooper

  54. PD Personalised Healthcare, Basel, Switzerland

    Naghmeh Ghazaleh

  55. University of Lausanne, Lausanne, Switzerland

    Jonas Richiardi

  56. ETH Zurich, Basel, Switzerland

    Damian Roqueiro

  57. F. Hoffmann–La Roche Ltd, Basel, Switzerland

    Diego Saldana Miranda

  58. Novartis Pharma AG, Basel, Switzerland

    Konstantinos Sechidis

  59. University of Lisbon, Lisbon, Portugal

    Guilherme Graça

Appendix

Appendix

Fig. 6.
figure 6

gSpan’s flattened exploration of \(\mathcal {L}_p\)

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Martin, T. et al. (2021). Towards Mining Generalized Patterns from RDF Data and a Domain Ontology. In: Kamp, M., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, vol 1524. Springer, Cham. https://doi.org/10.1007/978-3-030-93736-2_21

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  • DOI: https://doi.org/10.1007/978-3-030-93736-2_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93735-5

  • Online ISBN: 978-3-030-93736-2

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