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Conference of the Spanish Association for Artificial Intelligence

CAEPIA 2018: Advances in Artificial Intelligence pp 351–360Cite as

GraphDL: An Ontology for Linked Data Visualization

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11160))

Abstract

Linked Data is an increasingly important source of information and contextual knowledge in Data Science, and its appropriate visualization is key to effectively exploit them. This work presents an ontology to generate graph-based visualizations of Linked Data in a flexible and efficient way. The ontology has been used to successfully visualize DrugBank and DBPedia datasets in a large visualization environment.

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Notes

  1. 1.

    https://www.imperial.ac.uk/data-science/data-observatory/.

  2. 2.

    https://github.com/jgromero/graphdl.

  3. 3.

    http://sl.ugr.es/webgraphdl.

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Acknowledgements

Juan Gómez-Romero is supported by Universidad de Granada under the Young Researchers Fellowship Programme, and the Spanish Ministry of Education, Culture and Sport under the José Castillejo Research Stays Programme. Miguel Molina-Solana is supported by the EU’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 743623.

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Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, Univ. de Granada, Granada, Spain

    Juan Gómez-Romero

  2. Data Science Institute, Imperial College London, London, UK

    Juan Gómez-Romero & Miguel Molina-Solana

Authors
  1. Juan Gómez-Romero
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  2. Miguel Molina-Solana
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Corresponding author

Correspondence to Juan Gómez-Romero .

Editor information

Editors and Affiliations

  1. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Francisco Herrera

  2. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Sergio Damas

  3. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Rosana Montes

  4. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Sergio Alonso

  5. Andalusian Research Institute on Data Science and Computational Intelligence (DaSCI), University of Granada, Granada, Spain

    Óscar Cordón

  6. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Antonio González

  7. School of Engineering, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso

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Gómez-Romero, J., Molina-Solana, M. (2018). GraphDL: An Ontology for Linked Data Visualization. In: Herrera, F., et al. Advances in Artificial Intelligence. CAEPIA 2018. Lecture Notes in Computer Science(), vol 11160. Springer, Cham. https://doi.org/10.1007/978-3-030-00374-6_33

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  • DOI: https://doi.org/10.1007/978-3-030-00374-6_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00373-9

  • Online ISBN: 978-3-030-00374-6

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