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Transcript of © 2003 Eindhoven University of Technology Alexandru Telea, Flavius Frasincar, Geert-Jan Houben...
© 2003 Eindhoven University of Technology
Alexandru Telea, Flavius Frasincar,
Geert-Jan HoubenEindhoven University of Technology, the Netherlands
Visualizing RDF(s)-based Information
• What is RFD(s) data• Visualizing RDF(s) data• The Gviz tool• Applications• Conclusions
Overview
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 URIanonymous resource -literal name
Node Type Value
property URI
Edge Type Value
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
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
Previous Work
Visual tools: RDFSViz visualization tool
Uses AT&T GraphViz’s graph drawing to display RDF dataLimited to directed DAG drawing layouts
• visual
• insightful only for small datasets
• not easy to add ‘what-if’ queries and scenarios
Previous Work
Visual tools: OntoViz plugin for Protégé
Enhances Protégé with graph drawing capabilitiesSame (limited) directed layout as RDFSViz
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
Goal
Provide a visual examination and editing tool forRDF(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?
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
GViz Architecture Overview
RDF(s) data
selected data
displayed data
input
query
display &interaction
GViz Operation Pipeline
layout 1 (GEM) layout 2 (dot) type-colored glyphs
initial data
selected subset selected subset 2
... other operations …
Newspaper Example - Comparison
IsaViz GViz
yellow: literalsgreen: resources
red: subclassOfblue: typewhite: others
Nodes Edges
orange: nodes with a Property edge
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)
Applications
RDF(s) work data:
User Agent Profiles (UAProf) = RDF(s) datasetsdescribing mobile phone capabilities
Example:
UAProfschema
literals
resources
nodes with aProperty edge(towards literals)
subclassOfedges
Customizable selections
full schema only edges from/toclicked component
user clicks this component
customizing selection script: 18 Tcl linescustomizing glyphs script: 40 Tcl lines
Schema-instance comparisonschemaNokia 8310 instance instance vs schema
Use shape for type: named literals anonymousUse 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)
Instance-instance comparison
instance specificEricsson onlyall four phones
Color usage
similar overall structure
specific: literals, e.g.phone name, etc.Only one commonresource found!This led to discoveringan inconsistent namingscheme betweendatasets
Two Ericssons moresimilar than rest
Schema-schema comparison
schema specific2000, 2001 only2001, 2002 onlyall years
Color usage
little gray in (2000,2001), soschemas are very similar
no yellow!!! so nothing onlyin 2001 and 2002enough red in (2001,2002), sopart common to all years kept
2000 2001
2001 2002
2002 is a new product familywhich breaks the 2000-2001continuity
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 afterwardsNo need to develop new tool
Conclusions
A flexible graph visualization tool allowing easy end-usercustomization of most operations is essential
Spring-embedder layouts more effective than directedtrees/DAGs if combined with selection and glyphs
Need to look at• RDF(s) data editing• metrics for selection and glyph parametrization
Appendix: Mapping and Visualization
Map ‘abstract’ graph data to ‘concrete’ visual form
Mapping and visualization pipeline
Appendix: Mapping and Visualization
Basic Mapping
mappers data->2D/3D geometries
viewers geometries->display
glyphs parameters->geometries
glyph factories attributes->parameters
graphdata
mapper
Glyph factory
glyphs viewer
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
Appendix: Mapping and Visualization
Appendix: Mapping and Visualization
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