Eurographics/ IEEE-VGTC Symposium on Visualization 2008 A. Vilanova, A. Telea, G. Scheuermann, and T. Möller (Guest Editors) Volume 27 (2008), Number 3 From Web Data to Visualization via Ontology Mapping O. Gilson 1 , N. Silva 2 , P.W. Grant 1 and M. Chen 1 1 Department of Computer Science, Swansea University, UK 2 School of Engineering - Polytechnic of Porto, Portugal Abstract In this paper, we propose a novel approach for automatic generation of visualizations from domain-specific data available on the web. We describe a general system pipeline that combines ontology mapping and probabilistic reasoning techniques. With this approach, a web page is first mapped to a Domain Ontology, which stores the semantics of a specific subject domain (e.g., music charts). The Domain Ontology is then mapped to one or more Visual Representation Ontologies, each of which captures the semantics of a visualization style (e.g., tree maps). To enable the mapping between these two ontologies, we establish a Semantic Bridging Ontology, which specifies the appropriateness of each semantic bridge. Finally each Visual Representation Ontology is mapped to a visualization using an external visualization toolkit. Using this approach, we have developed a prototype software tool, SemViz, as a realisation of this approach. By interfacing its Visual Representation Ontologies with public domain software such as ILOG Discovery and Prefuse, SemViz is able to generate appropriate visualizations automatically from a large collection of popular web pages for music charts without prior knowledge of these web pages. Categories and Subject Descriptors (according to ACM CCS): I.3.0 [Computer Graphics]: General 1. Introduction Visualization is one of the indispensable means for address- ing the rapid explosion of data and information. Although a large collection of visualization techniques have been de- veloped over the past three decades, the majority of ordi- nary users, who handle data and information everyday, have little knowledge about these techniques. Despite there be- ing many interactive visualization tools (e.g., ILOG Discov- ery [BHS04], Prefuse [HCL05], Spotfire [Ahl96]) available in the public domain or commercially, producing visualiza- tions remains a skilled and time-consuming task. One approach for cost-effective dissemination of visual- ization techniques is to use captured expert knowledge for helping ordinary users generate visualizations automatically. To some users, this approach may serve as an introduction to new visualization techniques or an initial overview of pos- sible styles of visualizations, which is followed by a more intensive interaction to create finely-tuned and customised visualizations. To others, this approach may provide an ade- quate visualization service without consuming excessive ef- fort to learn and utilise the various visualization tools di- rectly. The method of “design galleries” [MAB ∗ 97], placed the first footprint in this direction. However, for visualizations with a very large parameter space, the method generally re- quires a long, iterative process involving the user before the search converges on a satisfactory result. In this work, we propose to use ontologies, which represent captured expert knowledge, to reduce the parameter space, providing a more effective automated solution to the dissemination of visual- ization techniques to ordinary users. As an example, we con- sider the visualization of music chart data on the web, and aim to generate visualizations automatically from the data. We present an ontology-based pipeline to map tabular data to geometrical data, and to select appropriate visualization tools, styles and parameters, producing formatted data that can be fed to the visualization tools automatically to gener- ate visualizations. The novel design of this pipeline features three ontologies, namely a domain ontology (DO) for storing domain knowl- edge about the source data (i.e., music charts in this work), a visual representation ontology (VRO) for storing the knowl- edge about visualization tools, styles and parameter space, and a semantic bridging ontology (SBO) for storing the knowledge about the mapping from DO to VRO. We use a c  2008 The Author(s) Journal compilation c 2008 The Eurographics Association and Blackwell Publishing Ltd. Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.