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LinkedPipes Applications - Automated Discovery of Configurable Linked Data Applications

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 12124)

Abstract

Consumption of Linked Data (LD) is a far less explored problem than its production. LinkedPipes Applications (LP-APPs) is a platform enabling data analysts and data journalists to easily create LD based applications such as, but not limited to, visualizations. It builds on our previous research regarding the automatic discovery of possible visualizations of LD. The approach was based on the matching of classes and predicates used in the data, e.g. in a form of a data sample, to what an application or visualization expects, e.g. in a form of a SPARQL query, solving potential mismatches in data by dynamically applying data transformers. In this demo, we present a platform that allows a data analyst to automatically discover possible visualizations of a given LD data source using this method and the applications contained in the platform. Next, the data analyst is able to configure the discovered visualization application and publish it or embed it in an arbitrary web page. Thanks to the configuration being stored in their Solid POD, multiple analysts are able to collaborate on a single application in a decentralized fashion. The resulting visualization application can be kept up to date via scheduling an ETL pipeline, regularly refreshing the underlying data.

Keywords

  • Linked data
  • Consumption
  • Visualization
  • Application
  • Solid

This work was supported by the Czech Science Foundation (GAČR), grant number 19-01641S.

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Fig. 1.

Notes

  1. 1.

    https://lod-cloud.net/.

  2. 2.

    https://github.com/OpenLinkSoftware/lod-cloud.

  3. 3.

    http://vos.openlinksw.com/owiki/wiki/VOS/VirtFacetBrowserInstallConfig.

  4. 4.

    https://github.com/linkedpipes/applications.

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Correspondence to Jakub Klímek .

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Klímek, J. et al. (2020). LinkedPipes Applications - Automated Discovery of Configurable Linked Data Applications. In: , et al. The Semantic Web: ESWC 2020 Satellite Events. ESWC 2020. Lecture Notes in Computer Science(), vol 12124. Springer, Cham. https://doi.org/10.1007/978-3-030-62327-2_25

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  • DOI: https://doi.org/10.1007/978-3-030-62327-2_25

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