Towards a Linked-Data 
Visualization Wizard 
Ghislain A. Atemezing (@gatemezing)* 
Raphaël Troncy (@rtroncy) 
(*) The auth...
Goal and Agenda 
§ Goal: Build a visualization wizard 
based on the RDF stack 
§ Motivation 
Ø Gap between traditional ...
Motivation 
§ Many structured datasets are now available on the 
Web (3 billions of Triples in the DBpedia 2014 release) ...
Challenges 
“Don’t ask what you can do for 
the Semantic Web; ask what 
The Semantic Web can do for 
you!” (D. Karger, MIT...
A Journey of a Web Application Developer 
§ Scenario 1: 
Ø Known Datasets, Known 
vocabularies à Specific 
SPARQL queri...
A Journey of a Web Application Developer 
§ Scenario 2: 
Ø Unknown Datasets, Known 
domains, so domain-specific 
SPARQL ...
A Journey of a Web Application Developer 
§ Scenario 3: 
Ø Unknown Datasets, Unknown 
domains, so generic SPARQL 
querie...
Our Proposal 
Linked Data 
Vizualization 
Wizard (LDVizWiz) 
2014/10/20 #COLD2014 – Riva del Garda, Italy - 8
Requirements of LDVizWiz (LDViz-”Wise”) 
§ Predefined categories associated 
to visual elements 
§ Build on top of RDF s...
Mapping Categories and vocabularies 
§ Geographic 
information 
Ø Geo, GeoSparql, etc. 
§ Temporal information 
Ø Time...
LDVizWiz Workflow 
2014/10/20 #COLD2014 – Riva del Garda, Italy - 11
Step 1: Categories detection 
§ Detection of main categories in datasets 
Ø ASK SPARQL queries on predefined categories ...
Experiment: Categories Detection 
Category Number % 
GEO DATA 97 21.84% 
EVENT DATA 16 3.60% 
TIME DATA 27 6.08% 
SKOS DAT...
Step2: Properties Aggregation 
§ Goal: Exploit the “connectors” between graphs 
§ “connectors” are used to enrich a give...
Step3: Publication 
§ Visualization Generator 
Ø Recommend the visual elements based on categories 
Ø Transform ASK que...
Current Implementation 
§ Javascript light version as “proof-of-concept” 
§ http://semantics.eurecom.fr/datalift/rdfViz/...
Conclusion and Future Work 
§ LDVizWiz: a tool to generate visualizations 
Ø Based on RDF standards, target to lay-users...
Questions? 
http://ww.slideshare.net/ghislainatemezing/cold2014-ldvizwiz
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cold2014-ldvizwiz

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Slides of the paper presented at #COLD2014 available at http://ceur-ws.org/Vol-1264/cold2014_AtemezingT.pdf, on building a Linked-data Visualization Wizard.

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cold2014-ldvizwiz

  1. 1. Towards a Linked-Data Visualization Wizard Ghislain A. Atemezing (@gatemezing)* Raphaël Troncy (@rtroncy) (*) The author thanks the Semantic Web Science Association (SWSA) for the grant receives to particiapte at ISWC, 2014.
  2. 2. Goal and Agenda § Goal: Build a visualization wizard based on the RDF stack § Motivation Ø Gap between traditional InfoVis tools and Semantic Web applications Ø Graphs are not meant to be shown to end-users § Current situation Ø Visualizations are built on known datasets and vocabularies Ø … what happen with unknown datasets and vocabularies? § Proposal: create generic visualizations based on data analysis of the RDF graphs § Conclusion and Perspectives 2014/10/20 #COLD2014 – Riva del Garda, Italy - 2
  3. 3. Motivation § Many structured datasets are now available on the Web (3 billions of Triples in the DBpedia 2014 release) § RDF is not what we show to end-users § InfoVis community has mature tools and studies on visualizing information § Triples are good … but they need to be “beautiful” for end-users § In the era of “structured big data”, we also need tools for Web–based visual analysis and reporting 2014/10/20 #COLD2014 – Riva del Garda, Italy - 3
  4. 4. Challenges “Don’t ask what you can do for the Semantic Web; ask what The Semantic Web can do for you!” (D. Karger, MIT CSAIL) – 1- How to build bridge to fill the gap between traditional InfoVis tools and Semantic Web technologies 2- How can Semantic Web help in visualization? 2014/10/20 #COLD2014 – Riva del Garda, Italy - 4
  5. 5. A Journey of a Web Application Developer § Scenario 1: Ø Known Datasets, Known vocabularies à Specific SPARQL queries Ø Visualizations: dataset specific § Example Ø Datasets on schools in France Ø Vocabularies: geo vocab, data cube, geometry. Ø Application: PerfectSchool 2014/10/20 #COLD2014 – Riva del Garda, Italy - 5
  6. 6. A Journey of a Web Application Developer § Scenario 2: Ø Unknown Datasets, Known domains, so domain-specific SPARQL queries Ø Visualizations: domain specific § Example Ø Endpoints of geo datasets Ø Domain: geospatial Ø Application: GeoRDFviz 2014/10/20 #COLD2014 – Riva del Garda, Italy - 6
  7. 7. A Journey of a Web Application Developer § Scenario 3: Ø Unknown Datasets, Unknown domains, so generic SPARQL queries Ø Visualizations: adapted to domains specific § Example Ø Any endpoints Ø Multiple domains: geodata, statistics, persons, cross-domains, etc.. Ø Application: ??? Related work on configuring Semantic Web widgets by data mapping [1] Application: Efficient search for Semantic News demonstrator in Cultural Heritage Dataset Tool: ClioPatria …but “method not apply to create interfaces on top of arbitrary SPARQL endpoints” [1] Hildebrand, Michiel, and Jacco Van Ossenbruggen. "Configuring semantic web interfaces by data mapping." Visual Interfaces to the Social and the Semantic Web (VISSW 2009) 443 (2009): 96. 2014/10/20 #COLD2014 – Riva del Garda, Italy - 7
  8. 8. Our Proposal Linked Data Vizualization Wizard (LDVizWiz) 2014/10/20 #COLD2014 – Riva del Garda, Italy - 8
  9. 9. Requirements of LDVizWiz (LDViz-”Wise”) § Predefined categories associated to visual elements § Build on top of RDF standards Ø e.g. SPARQL queries; Semantic Web technologies § Reuse existing Visualization libraries Ø e.g. Google Maps, Google Charts, D3.js, etc. § Input: Datasets published as LOD § Reuse Information Visualization Taxonomy § Target to non “RDF/SPARQL speakers” 2014/10/20 #COLD2014 – Riva del Garda, Italy - 9
  10. 10. Mapping Categories and vocabularies § Geographic information Ø Geo, GeoSparql, etc. § Temporal information Ø Time, interval ontologies § Event information Ø lode, event, sport, etc. § Agent/Person Ø foaf, org § Organization information Ø ORG vocabulary, vcard § Statistics information Ø Data cube, SDMX model § Knowledge information Ø Schemas, classifications using SKOS vocabulary 2014/10/20 #COLD2014 – Riva del Garda, Italy - 10
  11. 11. LDVizWiz Workflow 2014/10/20 #COLD2014 – Riva del Garda, Italy - 11
  12. 12. Step 1: Categories detection § Detection of main categories in datasets Ø ASK SPARQL queries on predefined categories Ø Uses well-known vocabularies in LOV Ø Unveil main facets of the visualizations Ø Condition the type of visual elements [1] 2014/10/20 #COLD2014 – Riva del Garda, Italy - 12 Detection [1] B. Shneiderman. The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. IEEE, 1996
  13. 13. Experiment: Categories Detection Category Number % GEO DATA 97 21.84% EVENT DATA 16 3.60% TIME DATA 27 6.08% SKOS DATA 02 0.45% ORG DATA 48 10.81% PERSON DATA 59 13.28% STAT DATA 29 6.6% Ø 444 endpoints (*) analyzed, 278 good answers (62.61%) using ASK queries. Ø Few taxonomies in SKOS, many GEO DATA § Applications Ø Automatic detection of endpoints categories Ø More “trustable” than human tagging Ø Map categories detected with “suitable” visual elements for the visualizations (e.g. TimeLine + maps for events data) (*) All the endpoints retrieved from sparqles.org 2014/10/20 #COLD2014 – Riva del Garda, Italy - 13
  14. 14. Step2: Properties Aggregation § Goal: Exploit the “connectors” between graphs § “connectors” are used to enrich a given graph Ø e.g. owl:sameAs, rdfs:seeAlso, skos:exactMatch § Retrieve properties from external datasets Ø So called “enriched properties” § Build candidate properties for visualization Ø For pop-up menus Ø For facet browsing Ø For charts display 2014/10/20 #COLD2014 – Riva del Garda, Italy - 14 Detection Aggregation
  15. 15. Step3: Publication § Visualization Generator Ø Recommend the visual elements based on categories Ø Transform ASK queries to SELECT or CONSTRUCT queries for input to visual library § Visualization Publisher Ø Export the description of a visualization in RDF Ø Add metadata for the visualization (charts) and the steps used to create it Ø e.g. dcat:Dataset, prov:wasDerivedFrom, void:ExampleResource, chart vocabulary 2014/10/20 #COLD2014 – Riva del Garda, Italy - 15 Detection Aggregation Publication
  16. 16. Current Implementation § Javascript light version as “proof-of-concept” § http://semantics.eurecom.fr/datalift/rdfViz/apps/ 2014/10/20 #COLD2014 – Riva del Garda, Italy - 16
  17. 17. Conclusion and Future Work § LDVizWiz: a tool to generate visualizations Ø Based on RDF standards, target to lay-users for graph analysis Ø Composed of 3 main steps: category detections, property aggregation and visualization publication § A Javascript implementation shows the usefulness of the approach § Future work Ø Extend categories and vocabularies for detection Ø Add more libraries for visual elements in visualizations Ø Provide templates for generating “mash-ups” that combine domains Ø Investigate the “importance” of a category within a dataset Ø Provide a user evaluation 2014/10/20 #COLD2014 – Riva del Garda, Italy - 17
  18. 18. Questions? http://ww.slideshare.net/ghislainatemezing/cold2014-ldvizwiz

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