An ontological approach to information visualization.

Visualization is one of the indispensable means for addressing the rapid explosion of data and information. Although a large collection of visualization techniques have been developed over the past three decades, the majority of ordinary users have little knowledge about these techniques. Despite there being many interactive visualization tools available in the public domain or commercially, producing visualizations remains a skilled and time-consuming task. One approach for cost-effective dissemination of visualization techniques is to use captured expert knowledge for helping ordinary users generate visualizations automatically. In this work, we propose to use captured knowledge in ontologies to reduce the parameter space, providing a more effective automated solution to the dissemination of visualization techniques to ordinary users. As an example, we consider the visualization of music chart data and football statistics on the web, and aim to generate visualizations automatically from the data. The work has three main contributions: Visualisation as Mapping. We consider the visualization process as a mapping task and assess this approach from both a tree-based and graph-based perspective. We discuss techniques for automatic mapping and present a general approach for Information Perceptualisation through mapping which we call Information Realisation. VizThis: Tree-centric Mapping. We have built a tree-based mapping toolkit which provides a pragmatic solution for visualising any XML-based source data using either SVG or X3D (or potentially any other XML-based target format). The toolkit has data cleansing and data analysis features. It also allows automatic mapping through a type-constrained system (AutoMap). If the user wishes to alter mappings, the system gives the users warnings about specific problem areas so that they can be immediately corrected. SeniViz: Graph-centric Mapping. We present an ontology-based pipeline to automatically map tabular data to geometrical data, and to select appropriate visualization tools, styles and parameters. The pipeline is based on three ontologies: a Domain Ontology (DO) captures the knowledge about the subject domain being visualized; a Visual Representation Ontology (VRO) captures the specific representational capabilities of different visualization techniques (e.g.. Tree Map); and a Semantic Bridge Ontology (SBO) captures specific expert-knowledge about valuable mappings between domain and representation concepts. In this way, we have an ontology mapping algorithm which can dynamically score and rank potential visualizations. We also present the results of a user study to assess the validity and effectiveness of the SemViz approach