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A Framework to Conduct and Report on Empirical User Studies in Semantic Web Contexts

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Knowledge Engineering and Knowledge Management (EKAW 2018)

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Abstract

Semantic Web technologies are being applied to increasingly diverse areas where user involvement is crucial. While a number of user interfaces for Semantic Web systems have become available in the past years, their evaluation and reporting often still suffer from weaknesses. Empirical evaluations are essential to compare different approaches, demonstrate their benefits and reveal their drawbacks, and thus to facilitate further adoption of Semantic Web technologies. In this paper, we review empirical user studies of user interfaces, visualizations and interaction techniques recently published at relevant Semantic Web venues, assessing both the user studies themselves and their reporting. We then chart the design space of available methods for user studies in Semantic Web contexts. Finally, we propose a framework for their comprehensive reporting, taking into consideration user expertise, experimental setup, task design, experimental procedures and results analysis.

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Notes

  1. 1.

    VOILA: International workshop series on “Visualization and Interaction for Ontologies and Linked Data”, see http://voila.visualdataweb.org.

  2. 2.

    The classified papers can be accessed at: http://survey.visualdataweb.org.

  3. 3.

    We use the term operation instead of task here to differentiate it from evaluation tasks.

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Acknowledgements

Catia Pesquita is funded by the Portuguese FCT through the LASIGE Strategic Project (UID/CEC/00408/2013), and also by FCT grant PTDC/EEI-ESS/4633/2014. Patrick Lambrix is funded by the Swedish e-Science Society (SeRC). Steffen Lohmann is partly funded by the Fraunhofer Cluster of Excellence Cognitive Internet Technologies (CIT).

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Pesquita, C., Ivanova, V., Lohmann, S., Lambrix, P. (2018). A Framework to Conduct and Report on Empirical User Studies in Semantic Web Contexts. In: Faron Zucker, C., Ghidini, C., Napoli, A., Toussaint, Y. (eds) Knowledge Engineering and Knowledge Management. EKAW 2018. Lecture Notes in Computer Science(), vol 11313. Springer, Cham. https://doi.org/10.1007/978-3-030-03667-6_36

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  • DOI: https://doi.org/10.1007/978-3-030-03667-6_36

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