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Balancing RDF Generation from Heterogeneous Data Sources

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The Semantic Web: ESWC 2022 Satellite Events (ESWC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13384))

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Abstract

Knowledge graphs in RDF are often generated from heterogeneous data sources to power services. However, knowledge graph generation is an unbalanced effort for producers compared to consumers of a knowledge graph. In this paper, I present my research about (i) investigating current RDF knowledge graph production and consumption approaches, and (ii) how to involve the consumer into a hybrid RDF generation approach to reduce the necessary resources for generating RDF for producers & consumers. I discuss the shortcomings of existing approaches for RDF generation from heterogeneous data sources (i.e., materialization and virtualization) and how I will address these: a Systematic Literature Review; an analysis and a set of guidelines for producers to select the right approach for an use case; and a combined hybrid approach to balance the producer’s and consumer’s effort in RDF generation. I already performed a Systematic Literature Review to get an overview of the existing approaches for RDF production from heterogeneous data sources. These results will be used to establish a set of producer guidelines, a benchmark to compare the current materialization and virtualization approaches, and evaluate the proposed hybrid approach. Thanks to my research, knowledge graph production and consumption will be more balanced and accessible to smaller companies and individuals. This way, they can focus on providing better services on top of a knowledge graph instead of being limited by the lack of computing resources to harvest enormous amounts of data from the Web and integrate it into a knowledge graph.

D. Van Assche—Supervised by Anastasia Dimou https://doi.org/0000-0003-2138-7972 & Ben De Meester https://doi.org/0000-0003-0248-0987.

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Notes

  1. 1.

    https://github.com/oeg-upm/morph-kgc.

  2. 2.

    https://virtuoso.openlinksw.com/.

  3. 3.

    https://www.websemanticsjournal.org/.

  4. 4.

    https://www.websemanticsjournal.org/.

  5. 5.

    https://icwe2021.webengineering.org/.

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Authors and Affiliations

  1. IDLab, Department of Electronics and Information Systems, Ghent University - -imec, Technologiepark -Zwijnaarde 122, 9052, Ghent, Belgium

    Dylan Van Assche

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  1. Dylan Van Assche
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Correspondence to Dylan Van Assche .

Editor information

Editors and Affiliations

  1. Faculty of Science, Informatics Institute, University of Amsterdam, Amsterdam, Noord-Holland, The Netherlands

    Paul Groth

  2. Department of Computer Engineering, University of Brescia, Brescia, Italy

    Anisa Rula

  3. School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL, USA

    Jodi Schneider

  4. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Ilaria Tiddi

  5. Bush House, Strand Campus, King’s College London, London, UK

    Elena Simperl

  6. Textkernel BV, Amsterdam, The Netherlands

    Panos Alexopoulos

  7. Elsevier BV, Amsterdam, The Netherlands

    Rinke Hoekstra

  8. FIZ Karlsruhe - Leibniz Institute for Information Infrastructure, Eggenstein-Leopoldshafen, Germany

    Mehwish Alam

  9. Department of Computer Science, KU Leuven, Sint-Katelijne-Waver, Belgium

    Anastasia Dimou

  10. Department of Computer Science, Aalto University, Espoo, Finland

    Minna Tamper

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Van Assche, D. (2022). Balancing RDF Generation from Heterogeneous Data Sources. In: Groth, P., et al. The Semantic Web: ESWC 2022 Satellite Events. ESWC 2022. Lecture Notes in Computer Science, vol 13384. Springer, Cham. https://doi.org/10.1007/978-3-031-11609-4_40

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  • DOI: https://doi.org/10.1007/978-3-031-11609-4_40

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