1. © 2003 Eindhoven University of Technology Alexandru Telea, Flavius Frasincar, Geert-Jan Houben Eindhoven University of Technology, the Netherlands Visualizing RDF(s)-based Information

2. What is RFD(s) data Visualizing RDF(s) data The Gviz tool Applications Conclusions Overview

3. What is RDF(s) data? RDF: resource description framework (http://www.w3.org) two graphs: instance and schema foundation for exchanging metadata describes web resources named resource URI anonymous resource - literal name Node Type Value property URI Edge Type Value

4. Questions and Requirements understand and modify the RDF(s) data understand: RDF(s) data = graphs graph understanding visualization modify: graph editing typical questions: how is a RDF(s) dataset looking? does an instance match a schema? how does an instance evolve? how does a schema evolve? need for visual graph analysis/editing tools

5. Previous Work Text-based tools: Protégé-2000 ‘Newspaper’ example: list of articles, sections, employees, advertising in a fictitious newspaper. text-only insightful only for small datasets not easy to add ‘what-if’ queries and scenarios

6. Previous Work Visual tools: RDFSViz visualization tool Uses AT&T GraphViz’s graph drawing to display RDF data Limited to directed DAG drawing layouts visual insightful only for small datasets not easy to add ‘what-if’ queries and scenarios

7. Previous Work Visual tools: OntoViz plugin for Protégé Enhances Protégé with graph drawing capabilities Same (limited) directed layout as RDFSViz

8. Previous Work Visual tools: IsaViz visual set of graph editing tools insightful only for small datasets not easy to add ‘what-if’ queries and scenarios

9. Goal Provide a visual examination and editing tool for RDF(s) data that: copes with realistically large datasets allows an easy definition of new queries allows an easy definition of new visualizations (layouts, coloring schemes and shapes, etc) Can we reuse/adapt an existing tool?

10. The GViz Tool first used in the context of reverse engineering (thus handles large graphs) (VisSym’02, IWPC’02, TOOLSEE ’02) generic data and operation model allows end-user customization of all operations: - selection: what to display - layout: how to arrange - glyphs: what to draw - interaction: how to respond

11. GViz Architecture Overview RDF(s) data selected data displayed data input query display & interaction

12. GViz Operation Pipeline layout 1 (GEM) layout 2 (dot) type-colored glyphs initial data selected subset selected subset 2... other operations …

13. Newspaper Example - Comparison IsaViz GViz yellow: literals green: resources red: subclassOf blue: type white: others NodesEdges orange: nodes with a Property edge

14. Applications customizable selections schema-instance comparisons - how/what of a schema is instanced instances comparison - how do two instances (of same schema) differ schemas comparison - how do two schemas differ (e.g. schema evolution) graph comparison operations (done only for non-anonymous nodes)

15. Applications RDF(s) work data: User Agent Profiles (UAProf) = RDF(s) datasets describing mobile phone capabilities Example: UAProf schema literals resources nodes with a Property edge (towards literals) subclassOf edges

16. Customizable selections full schemaonly edges from/to clicked component user clicks this component customizing selection script: 18 Tcl lines customizing glyphs script: 40 Tcl lines

17. Schema-instance comparison schemaNokia 8310 instance instance vs schema Use shape for type: named literals anonymous Use color for comparison: instance schema common Most instance-specific nodes are literals (yellow, ) Only the (few) component-types are instantiated (red, ) Many uninstantiated properties (green)

18. Instance-instance comparison instance specific Ericsson only all four phones Color usage similar overall structure specific: literals, e.g. phone name, etc. Only one common resource found! This led to discovering an inconsistent naming scheme between datasets Two Ericssons more similar than rest

19. Schema-schema comparison schema specific 2000, 2001 only 2001, 2002 only all years Color usage little gray in (2000,2001), so schemas are very similar no yellow!!! so nothing only in 2001 and 2002 enough red in (2001,2002), so part common to all years kept 20002001 2002 2002 is a new product family which breaks the 2000-2001 continuity

20. Conclusions Combination of customizable selections, glyphs, layouts, and interaction is very effective for understanding RDF(s) datasets Facts found by visualization (and previously unknown): naming scheme changes mobile phone instances for different schemas are similar product family breakpoint in schema evolution Effort needed to adapt Gviz tool to RDF(s) data & tasks: 10-40 Tcl lines per task 30 minutes for the first task, 5-10 minutes afterwards No need to develop new tool

21. Conclusions A flexible graph visualization tool allowing easy end-user customization of most operations is essential Spring-embedder layouts more effective than directed trees/DAGs if combined with selection and glyphs Need to look at RDF(s) data editing metrics for selection and glyph parametrization

23. Appendix: Mapping and Visualization Map ‘abstract’ graph data to ‘concrete’ visual form Mapping and visualization pipeline

24. Appendix: Mapping and Visualization Basic Mapping mappers data->2D/3D geometries viewers geometries->display glyphs parameters->geometries glyph factories attributes->parameters graph data mapper Glyph factory glyphsviewer

25. Appendix: Mapping and Visualization Glyphs similar to the SciVis glyphs 2D/3D parametrizable graphical objects implemented as (small) Inventor scene graphs Glyph Factories called by mappers for each node/edge to map written as (small) Tcl scripts, thus very easy to customize selectable/editable at run-time to map data in various ways

26. Appendix: Mapping and Visualization

27. Advantages of the chosen architecture: easy to produce different mappings on the fly (average Tcl glyph factory < 15 lines of code) flexible (control mapping at node/edge level) simple to implement (2 mappers vs >20 in SciViz) adding more complex mappers could e.g. produce UML-like diagrams automatically