Skip to main content

A Conceptual Modelling Approach to Visualising Linked Data

  • Conference paper
  • First Online:
On the Move to Meaningful Internet Systems: OTM 2019 Conferences (OTM 2019)

Abstract

Increasing numbers of Linked Open Datasets are being published, and many possible data visualisations may be appropriate for a user’s given exploration or analysis task over a dataset. Users may therefore find it difficult to identify visualisations that meet their data exploration or analyses needs. We propose an approach that creates conceptual models of groups of commonly used data visualisations, which can be used to analyse the data and users’ queries so as to automatically generate recommendations of possible visualisations. To our knowledge, this is the first work to propose a conceptual modelling approach to recommending visualisations for Linked Data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.tableau.com/products/desktop.

  2. 2.

    https://github.com/d3/d3/wiki/Gallery.

  3. 3.

    https://developers.google.com/chart/interactive/docs/examples.

  4. 4.

    http://graphdb.ontotext.com/.

  5. 5.

    https://www.tableau.com/products/desktop.

References

  1. Andrienko, N., Andrienko, G., Gatalsky, P.: Exploratory spatio-temporal visualization: an analytical review. Vis. Lang. Comput. 14(6), 503–541 (2003)

    Article  Google Scholar 

  2. Arenas, M., Grau, B.C., Kharlamov, E., Marciuska, S., Zheleznyakov, D., Jimenez-Ruiz, E.: SemFacet: semantic faceted search over Yago. In: International Conference on World Wide Web, pp. 123–126. ACM (2014)

    Google Scholar 

  3. Atemezing, G.A., Troncy, R.: Towards a linked-data based visualization wizard. In: COLD (2014)

    Google Scholar 

  4. Auer, S., Dietzold, S., Riechert, T.: OntoWiki – a tool for social, semantic collaboration. In: Cruz, I., et al. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 736–749. Springer, Heidelberg (2006). https://doi.org/10.1007/11926078_53

    Chapter  Google Scholar 

  5. Benedetti, F., Bergamaschi, S., Po, L.: A visual summary for linked open data sources. In: ISWC, vol. 1272, pp. 173–176 (2014)

    Google Scholar 

  6. Berners-Lee, T., et al.: Tabulator: exploring and analyzing linked data on the semantic web. In: 3rd International Semantic Web User Interaction Workshop, p. 159 (2006)

    Google Scholar 

  7. Bikakis, N., Liagouris, J., Krommyda, M., Papastefanatos, G., Sellis, T.: graphVizdb: a scalable platform for interactive large graph visualization. In: ICDE, pp. 1342–1345. IEEE (2016)

    Google Scholar 

  8. Bikakis, N., Sellis, T.: Exploration and visualization in the web of big linked data: a survey of the state of the art. arXiv preprint arXiv:1601.08059 (2016)

  9. Bikakis, N., Papastefanatos, G., Skourla, M., Sellis, T.: A hierarchical aggregation framework for efficient multilevel visual exploration and analysis. Seman. Web 8(1), 139–179 (2017)

    Article  Google Scholar 

  10. Brunetti, J.M., Auer, S., García, R.: The Linked Data Visualization Model. In: ISWC (2012)

    Google Scholar 

  11. Card, S.K., Mackinlay, J.: The structure of the information visualization design space. In: Proceedings of Information Visualization, pp. 92–99. IEEE (1997)

    Google Scholar 

  12. Dadzie, A.-S., Rowe, M.: Approaches to visualising linked data: a survey. Seman. Web 2(2), 89–124 (2011)

    Article  Google Scholar 

  13. Fu, B., Noy, N.F., Storey, M.-A.: Eye tracking the user experience - an evaluation of ontology visualization techniques. Seman. Web 8(1), 23–41 (2017)

    Article  Google Scholar 

  14. Gilson, O., Silva, N., Grant, P.W., Chen, M.: From web data to visualization via ontology mapping. Comput. Graph. Forum 27(3), 959–966 (2008)

    Article  Google Scholar 

  15. Gorodov, E.Y., Gubarev, V.V.: Analytical review of data visualization methods in application to big data. Electr. Comput. Eng. 2013, 22 (2013)

    Google Scholar 

  16. Graziosi, A., Di Iorio, A., Poggi, F., Peroni, S.: Customised visualisations of linked open data. In: VOILA@ISWC, pp. 20–33 (2017)

    Google Scholar 

  17. Harth, A.: VisiNav: a system for visual search and navigation on web data. Web Seman. 8(4), 348–354 (2010). Science Services and Agents on the World Wide Web

    Article  Google Scholar 

  18. Heim, P., Lohmann, S., Tsendragchaa, D., Ertl, T.: SemLens: visual analysis of semantic data with scatter plots and semantic lenses. In: 7th International Conference on Semantic Systems, pp. 175–178. ACM (2011)

    Google Scholar 

  19. Heim, P., Ziegler, J., Lohmann, S.: gFacet: a browser for the web of data. In: International Workshop on Interacting with Multimedia Content in the Social Semantic Web, IMC-SSW 2008, vol. 417, pp. 49–58. Citeseer (2008)

    Google Scholar 

  20. Mackinlay, J.: Automating the design of graphical presentations of relational information. Trans. Graph. 5(2), 110–141 (1986)

    Article  Google Scholar 

  21. Kämpgen, B., Harth, A.: OLAP4LD – a framework for building analysis applications over governmental statistics. In: Presutti, V., et al. (eds.) ESWC 2014. LNCS, vol. 8798, pp. 389–394. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11955-7_54

    Chapter  Google Scholar 

  22. Klímek, J., Helmich, J., Nečaský, M.: Payola: collaborative linked data analysis and visualization framework. In: Cimiano, P., Fernández, M., Lopez, V., Schlobach, S., Völker, J. (eds.) ESWC 2013. LNCS, vol. 7955, pp. 147–151. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41242-4_14

    Chapter  Google Scholar 

  23. Kremen, P., Saeeda, L., Blaško, M.: Dataset dashboard - a SPARQL endpoint explorer. In: International Workshop on Visualization and Interaction for Ontologies and Linked Data, VOILA 2018 (2018)

    Google Scholar 

  24. Leskinen, P., Miyakita, G., Koho, M., Hyvönen, E., et al.: Combining faceted search with data-analytic visualizations on top of a sparql endpoint. In: International Workshop on Visualization and Interaction for Ontologies and Linked Data, VOILA 2018. CEUR-WS.org (2018)

    Google Scholar 

  25. Lohmann, S., Link, V., Marbach, E., Negru, S.: WebVOWL: web-based visualization of ontologies. In: Lambrix, P., et al. (eds.) EKAW 2014. LNCS (LNAI), vol. 8982, pp. 154–158. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-17966-7_21

    Chapter  Google Scholar 

  26. Lohmann, S., Negru, S., Haag, F., Ertl, T.: Visualizing ontologies with VOWL. Seman. Web 7(4), 399–419 (2016)

    Article  Google Scholar 

  27. Mackinlay, J., Hanrahan, P., Stolte, C.: Show me: automatic presentation for visual analysis. Trans. Visual Comput. Graph. 13(6), 1137–1144 (2007)

    Article  Google Scholar 

  28. Martin, M., Abicht, K., Stadler, C., Ngonga Ngomo, A.-C., Soru, T., Auer, S.: CubeViz: exploration and visualization of statistical linked data. In: International Conference on World Wide Web, pp 219–222. ACM (2015)

    Google Scholar 

  29. May, W.: Information extraction and integration with Florid: the Mondial case study. Technical report 131, Universität Freiburg, Institut für Informatik (1999)

    Google Scholar 

  30. McBrien, P., Poulovassilis, A.: Towards data visualisation based on conceptual modelling. In: Trujillo, J.C., et al. (eds.) ER 2018. LNCS, vol. 11157, pp. 91–99. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00847-5_8

    Chapter  Google Scholar 

  31. Nuzzolese, A.G., Presutti, V., Gangemi, A., Peroni, S., Ciancarini, P.: Aemoo: linked data exploration based on knowledge patterns. Seman. Web 8(1), 87–112 (2017)

    Article  Google Scholar 

  32. Peña, O., Aguilera, U., López-de-Ipiña, D.: Linked open data visualization revisited: a survey. Seman. Web J. (2014)

    Google Scholar 

  33. R Core Team: R: a Language and Environment for Statistical Computing. R Foundation for Statistical Computing (2013)

    Google Scholar 

  34. Ristoski, P., Paulheim, H.: Visual analysis of statistical data on maps using linked open data. In: Gandon, F., et al. (eds.) ESWC 2015. LNCS, vol. 9341, pp. 138–143. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25639-9_27

    Chapter  Google Scholar 

  35. Roth, S.F., Kolojejchick, J., Mattis, J., Goldstein, J.: Interactive graphic design using automatic presentation knowledge. In: Proceedings of CHI, pp. 112–117. ACM (1994)

    Google Scholar 

  36. Shneiderman, B.: The eyes have it: a task by data type taxonomy for information visualizations. In: The Craft of Information Visualization, pp. 364–371. Morgan Kaufmann (2003)

    Google Scholar 

  37. Stadler, C., Martin, M., Auer, S.: Exploring the web of spatial data with facete. In: International Conference on World Wide Web, pp. 175–178. ACM (2014)

    Google Scholar 

  38. Stolte, C., Tang, D., Hanrahan, P.: Polaris: a system for query, analysis, and visualization of multidimensional relational databases. Trans. Visual Comput. Graphics 8(1), 52–65 (2002)

    Article  Google Scholar 

  39. Telea, A.C.: Data Visualization: Principles and Practice. CRC Press, Boca Raton (2014)

    Book  Google Scholar 

  40. Thellmann, K., Galkin, M., Orlandi, F., Auer, S.: LinkDaViz – automatic binding of linked data to visualizations. In: Arenas, M., et al. (eds.) ISWC 2015. LNCS, vol. 9366, pp. 147–162. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25007-6_9

    Chapter  Google Scholar 

  41. Tory, M., Moller, T.: Rethinking visualization: a high-level taxonomy. In: Proceedings of Information Visualization, pp. 151–158. IEEE (2004)

    Google Scholar 

  42. Tschinkel, G., Veas, E.E., Mutlu, B., Sabol, V.: Using semantics for interactive visual analysis of linked open data. In: ISWC, pp. 133–136 (2014)

    Google Scholar 

  43. Ward, M.O., Grinstein, G., Keim, D.: Interactive Data Visualization: Foundations, Techniques, and Applications. CRC Press, Boca Raton (2010)

    Book  MATH  Google Scholar 

  44. Ware, C.: Information Visualization: Perception for Design, 3rd edn. Morgan Kaufmann, Burlington (2013)

    Google Scholar 

  45. Weise, M., Lohmann, S., Haag, F.: Extraction and visualization of TBox information from SPARQL endpoints. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds.) EKAW 2016. LNCS (LNAI), vol. 10024, pp. 713–728. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49004-5_46

    Chapter  Google Scholar 

  46. Wilkinson, L.: The Grammar of Graphics. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  47. Wills, G., Wilkinson, L.: AutoVis: automatic visualization. Inf. Visual. 9(1), 47–69 (2010)

    Article  Google Scholar 

  48. Wongsuphasawat, K., et al.: Voyager: exploratory analysis via faceted browsing of visualization recommendations. Trans. Visual Comput. Graphics 22(1), 649–658 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Peter McBrien .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

McBrien, P., Poulovassilis, A. (2019). A Conceptual Modelling Approach to Visualising Linked Data. In: Panetto, H., Debruyne, C., Hepp, M., Lewis, D., Ardagna, C., Meersman, R. (eds) On the Move to Meaningful Internet Systems: OTM 2019 Conferences. OTM 2019. Lecture Notes in Computer Science(), vol 11877. Springer, Cham. https://doi.org/10.1007/978-3-030-33246-4_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33246-4_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33245-7

  • Online ISBN: 978-3-030-33246-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics