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On the Mental Workload Assessment of Uplift Mapping Representations in Linked Data

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Human Mental Workload: Models and Applications (H-WORKLOAD 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1012))

Abstract

Self-reporting procedures have been largely employed in literature to measure the mental workload experienced by users when executing a specific task. This research proposes the adoption of these mental workload assessment techniques to the task of creating uplift mappings in Linked Data. A user study has been performed to compare the mental workload of “manually” creating such mappings, using a formal mapping language and a text editor, to the use of a visual representation, based on the block metaphor, that generate these mappings. Two subjective mental workload instruments, namely the NASA Task Load Index and the Workload Profile, were applied in this study. Preliminary results show the reliability of these instruments in measuring the perceived mental workload for the task of creating uplift mappings. Results also indicate that participants using the visual representation achieved smaller and more consistent scores of mental workload.

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Notes

  1. 1.

    http://www.w3.org/TR/rdf11-concepts/.

  2. 2.

    http://lod-cloud.net/

  3. 3.

    http://xmlns.com/foaf/0.1/

  4. 4.

    https://scratch.mit.edu/, last accessed May 2018

  5. 5.

    TURTLE is only one of the many standardized RDF representations. TURTLE was chosen as it is terse, and one of the more usable and easier to read representations. Even the R2RML W3C Recommendation uses TURTLE for their examples.

  6. 6.

    Available at https://www.scss.tcd.ie/~crottija/juma/r2rml.pdf and https://www.youtube.com/watch?v=fn5mKGGj2us.

  7. 7.

    Available at https://www.scss.tcd.ie/~crottija/juma/juma.pdf and https://www.youtube.com/watch?v=Q97YeZtu_tA.

  8. 8.

    Available at https://github.com/dalers/mywind.

  9. 9.

    http://xmlns.com/foaf/0.1/.

  10. 10.

    http://www.w3.org/2000/01/rdf-schema.

  11. 11.

    https://jena.apache.org/, accessed May 2018.

  12. 12.

    https://github.com/antidot/db2triples, accessed in May 2018.

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Acknowledgements

This paper was supported by CNPQ, National Counsel of Technological and Scientific Development – Brazil and by the Science Foundation Ireland (Grant 13/RC/2106) as part of the ADAPT Centre for Digital Content Technology (http://www.adaptcentre.ie/) at Trinity College Dublin.

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Authors and Affiliations

  1. ADAPT Centre, Trinity College Dublin, Dublin 2, Ireland

    Ademar Crotti Junior, Christophe Debruyne & Declan O’Sullivan

  2. ADAPT Centre, Dublin Institute of Technology, Dublin 8, Ireland

    Luca Longo

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  1. Ademar Crotti Junior
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  2. Christophe Debruyne
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Correspondence to Ademar Crotti Junior .

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Editors and Affiliations

  1. Dublin Institute of Technology, Dublin, Ireland

    Luca Longo

  2. Dublin Institute of Technology, Dublin, Ireland

    M. Chiara Leva

Appendix A: MWL Questionnaires

Appendix A: MWL Questionnaires

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Junior, A.C., Debruyne, C., Longo, L., O’Sullivan, D. (2019). On the Mental Workload Assessment of Uplift Mapping Representations in Linked Data. In: Longo, L., Leva, M. (eds) Human Mental Workload: Models and Applications. H-WORKLOAD 2018. Communications in Computer and Information Science, vol 1012. Springer, Cham. https://doi.org/10.1007/978-3-030-14273-5_10

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