Data VisualisationOWL/RDF Ontologies for DV on the Web IntroductionFinding examples of data visualisation images on the Web is a simple task, but no RDF metadata vocabulary currently exists to describe the structure of a visualisation. This work identifies an important area of research of using Linked Data vocabularies to describe data visualisations on the Web, enabling the image content of data visualisations to be properly linked to datasets and other RDF resources for the first time.

Ontolog y DesignThe findings of this work reveal faceted aspects of data visualisation images, including visual elements (colour, shape etc), geometry (point, line, area), datasets, domain concepts for specialist data (i.e. in temporal, spatial and population data), and the structure of data visualisation images. In response to these structural and functional elements three modular ontologies are devised, GGON – the Grammar of Graphics Ontology, DVCON – for Data Visualisation Concepts, and DVO – the Data Visualisation Ontology which holds the core classes and combines the other three ontologies (in its complete form).

DV and the WebBy nature of their format, images, applications and videos cannot be searched as completely as data documents and require additional metadata to provide additional information, which can be referenced and served to users. It is essential to identify resources in order to share, modify and reason about them (Berners‐Lee et al., 2006 p.8).

Data V isualisation TaxonomiesA review of papers on visualisation taxonomies provided information on the structure and function of these images. These taxonomies include visualisation tasks (Shneiderman, 1996), the classification of image types (Lohse, Biolsi and Walker, 1994), structure (Card and Mackinlay, 1997), categorical data (Bendix, Kosara and Hauser, 2005), visual clutter reduction (Dix, 2007), mapping data to images (Ziemkiewicz and Kosara, 2009), and visual features (Nazemi et al., 2011).

Graphic ElementsThe transformation of data to visual form can be described as encoding, which is data made visual by a mark. This mark is itself considered a retinal variable applied to a geometric form of point, line, or area (Bertin, 1983). The retinal variables described by Bertin can be seen in Figure 1 which were later extended by Mackinlay (1986) and Card and Mackinlay (1997).

T he Data V isualisation RDF/OWL Ontolog yThe DV ontology aims to provide a formal language for describing an existing graphical rendering of a geometric construction which visualises a dataset.

Competency Questions• What images are inferred members of the Time Cube class?• How can data about population location over time be visualised? • What dataset is the World Hunger Index image visualising? • Which data visualisations feature time as a concept?

Data V isualisation Ontolog y by Example

Current WorkCurrent PhD research project aims to investigate the potential application of microeconomic theory and machine learning techniques to visualisation Open Data, in which a user is guided in selecting an appropriate visualisation based both on their preferences and on the utility of specific visualisations in the context of different user tasks and domains. Development work requires evaluating the logic of human - and personal - visual perception and the potential for integratation with existing web technologies such as the Javascript visualization library D3.js. If the logic of perception is definable, a system based on the structural aspects of the DV Ontology could not only infer an appropriate visualisation but also make efficiency-based decisions about how each invariant and component can best be displayed, all while the user is interacting with the system. These efforts move towards a holistic view of visualization (see Ziemkiewicz and Kosara, 2010), which must seek to address questions and relationships between structure and function of these images.

Paul Booth | pmb1g11@soton.ac.uk

ReferencesBerners-Lee, T. et al., 2006. A Framework for Web Science. Foundations and Trends in Web Science, 1(1), pp.1–130.

Bendix, F., Kosara, R. & Hauser, H., 2005. Parallel sets: Visual analysis of categorical data, INFOVIS 2005. IEEE Symposium on Information Visualization, pp.133–140.

Bertin, J., 1983. Semiology of graphics: diagrams, networks, maps. University of Wisconsin Press.

Card, S.K. & Mackinlay, J., 1997. The structure of the information visualization design space, Proceedings of the IEEE Symposium on Information Visualization, pp.92–99. Lohse, G.L., Biolsi, K. & Walker, N., 1994. A classification of visual representations, Communications of the ACM 37(12), pp.36-49.

Mackinlay, J., 1986. Automating the design of graphical presentations of relational information. ACM Transactions on Graphics, 5(2), pp.110–141.

Nazemi, K., Breyer, M. & Kuijper, A., 2011. User-oriented graph visualization taxonomy: a data-oriented examination of visual features. Human Centered Design, pp.576–585.

Shneiderman, B., 1996. The eyes have it: A task by data type taxonomy for information visualizations. Visual Languages, 1996. Proceedings., IEEE Symposium on, pp.336–343.

Ziemkiewicz, C. & Kosara, R., 2009. Embedding information visualization within visual representation. Advances in Information and Intelligent Systems, pp.307–326.

Ziemkiewicz, C. & Kosara, R., 2010. Beyond Bertin: Seeing the Forest despite the Trees T. M. Rhyne, ed. Computer Graphics and Applications, IEEE, 30(5), pp.6–10.Web Science

Figure 2: Time-Cube, visually reinterpreted for display purposes this type of visualisation provided a suitable challenge for the DV Ontologies to adequately describe.

Figure 3: Diagram of structure and links relating to Figure 2 (a Time Cube). This conveys the simplicity and effectiveness of the DV Ontology for providing meta-data for visualisations, which can be used for images of any dimensional complexity at varying levels of granularity.

Figure 1: Diagram showing a selection Bertin’s original visual variables*, later adapted by Jock Mackinlay. A ranking of of efficiency of visual variables following a series of studies by Mackinlay in 1986.

POSITION

COLOUR

AREA

VALUE

SHAPE

ANGLE

LENGTH

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ST A

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T AC

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POSIT ION

LENGTH

LENGTHANGLE

ANGLEAREA

AREA

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COLOUR

SHAPE

SHAPE

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V I S U A L V A R I A B L E S J. Bertin’s selection of original visual variables are marked