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BioGrakn: A Knowledge Graph-Based Semantic Database for Biomedical Sciences

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Complex, Intelligent, and Software Intensive Systems (CISIS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 611))

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Abstract

The proliferation of biological research data generated and shared openly online is of huge benefit to the scientific community, but there are often significant challenges to overcome before it can be integrated from different sources and re-used to gain new knowledge. This paper introduces BioGrakn, which is a graph-based deductive database, combining the power of knowledge graphs and machine reasoning. BioGrakn illustrates how data can be aggregated and integrated, modelled in all its complexity and contextual specificity, and extended as needed. Built upon GRAKN.AI, it provides an integrated, intelligent database for researchers handling complex data.

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Notes

  1. 1.

    Further information about syntax and keywords used by Graql can be found in https://grakn.ai/pages/documentation/graql/graql-overview.html.

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Authors and Affiliations

  1. ICAR-CNR, via Ugo La Malfa 153, 90146, Palermo, Italy

    Antonio Messina & Alfonso Urso

  2. Grakn Labs Ltd, Unit 22, 8 Hornsey Street, London, UK

    Haikal Pribadi, Jo Stichbury, Michelangelo Bucci & Szymon Klarman

Authors
  1. Antonio Messina
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  2. Haikal Pribadi
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  3. Jo Stichbury
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  4. Michelangelo Bucci
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  5. Szymon Klarman
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  6. Alfonso Urso
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Corresponding author

Correspondence to Antonio Messina .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Politècnico di Torino, Istituto Superiore Mario Boella, Turin, Italy

    Olivier Terzo

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Messina, A., Pribadi, H., Stichbury, J., Bucci, M., Klarman, S., Urso, A. (2018). BioGrakn: A Knowledge Graph-Based Semantic Database for Biomedical Sciences. In: Barolli, L., Terzo, O. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2017. Advances in Intelligent Systems and Computing, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-319-61566-0_28

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  • DOI: https://doi.org/10.1007/978-3-319-61566-0_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61565-3

  • Online ISBN: 978-3-319-61566-0

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