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cold2014-ldvizwiz

Ghislain Atemezing
Ghislain Atemezing

際際滷s 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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Towards a Linked-Data Visualization Wizard Ghislain A. Atemezing (@gatemezing)* Rapha谷l Troncy (@rtroncy) (*) The author thanks the Semantic Web Science Association (SWSA) for the grant receives to particiapte at ISWC, 2014.
Goal and Agenda 則 Goal: Build a visualization wizard based on the RDF stack 則 Motivation Gap between traditional InfoVis tools and Semantic Web applications Graphs are not meant to be shown to end-users 則 Current situation Visualizations are built on known datasets and vocabularies what happen with unknown datasets and vocabularies? 則 Proposal: create generic visualizations based on data analysis of the RDF graphs 則 Conclusion and Perspectives 2014/10/20 #COLD2014 Riva del Garda, Italy - 2
Motivation 則 Many structured datasets are now available on the Web (3 billions of Triples in the DBpedia 2014 release) 則 RDF is not what we show to end-users 則 InfoVis community has mature tools and studies on visualizing information 則 Triples are good but they need to be beautiful for end-users 則 In the era of structured big data, we also need tools for Webbased visual analysis and reporting 2014/10/20 #COLD2014 Riva del Garda, Italy - 3
Challenges Dont 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
A Journey of a Web Application Developer 則 Scenario 1: Known Datasets, Known vocabularies Specific SPARQL queries Visualizations: dataset specific 則 Example Datasets on schools in France Vocabularies: geo vocab, data cube, geometry. Application: PerfectSchool 2014/10/20 #COLD2014 Riva del Garda, Italy - 5
A Journey of a Web Application Developer 則 Scenario 2: Unknown Datasets, Known domains, so domain-specific SPARQL queries Visualizations: domain specific 則 Example Endpoints of geo datasets Domain: geospatial Application: GeoRDFviz 2014/10/20 #COLD2014 Riva del Garda, Italy - 6
A Journey of a Web Application Developer 則 Scenario 3: Unknown Datasets, Unknown domains, so generic SPARQL queries Visualizations: adapted to domains specific 則 Example Any endpoints Multiple domains: geodata, statistics, persons, cross-domains, etc.. 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
Our Proposal Linked Data Vizualization Wizard (LDVizWiz) 2014/10/20 #COLD2014 Riva del Garda, Italy - 8
Requirements of LDVizWiz (LDViz-Wise) 則 Predefined categories associated to visual elements 則 Build on top of RDF standards e.g. SPARQL queries; Semantic Web technologies 則 Reuse existing Visualization libraries e.g. Google Maps, Google Charts, D3.js, etc. 則 Input: Datasets published as LOD 則 Reuse Information Visualization Taxonomy 則 Target to non RDF/SPARQL speakers 2014/10/20 #COLD2014 Riva del Garda, Italy - 9
Mapping Categories and vocabularies 則 Geographic information Geo, GeoSparql, etc. 則 Temporal information Time, interval ontologies 則 Event information lode, event, sport, etc. 則 Agent/Person foaf, org 則 Organization information ORG vocabulary, vcard 則 Statistics information Data cube, SDMX model 則 Knowledge information Schemas, classifications using SKOS vocabulary 2014/10/20 #COLD2014 Riva del Garda, Italy - 10
LDVizWiz Workflow 2014/10/20 #COLD2014 Riva del Garda, Italy - 11
Step 1: Categories detection 則 Detection of main categories in datasets ASK SPARQL queries on predefined categories Uses well-known vocabularies in LOV Unveil main facets of the visualizations 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
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% 444 endpoints (*) analyzed, 278 good answers (62.61%) using ASK queries. Few taxonomies in SKOS, many GEO DATA 則 Applications Automatic detection of endpoints categories More trustable than human tagging 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
Step2: Properties Aggregation 則 Goal: Exploit the connectors between graphs 則 connectors are used to enrich a given graph e.g. owl:sameAs, rdfs:seeAlso, skos:exactMatch 則 Retrieve properties from external datasets So called enriched properties 則 Build candidate properties for visualization For pop-up menus For facet browsing For charts display 2014/10/20 #COLD2014 Riva del Garda, Italy - 14 Detection Aggregation
Step3: Publication 則 Visualization Generator Recommend the visual elements based on categories Transform ASK queries to SELECT or CONSTRUCT queries for input to visual library 則 Visualization Publisher Export the description of a visualization in RDF Add metadata for the visualization (charts) and the steps used to create it e.g. dcat:Dataset, prov:wasDerivedFrom, void:ExampleResource, chart vocabulary 2014/10/20 #COLD2014 Riva del Garda, Italy - 15 Detection Aggregation Publication
Current Implementation 則 Javascript light version as proof-of-concept 則 http://semantics.eurecom.fr/datalift/rdfViz/apps/ 2014/10/20 #COLD2014 Riva del Garda, Italy - 16
Conclusion and Future Work 則 LDVizWiz: a tool to generate visualizations Based on RDF standards, target to lay-users for graph analysis Composed of 3 main steps: category detections, property aggregation and visualization publication 則 A Javascript implementation shows the usefulness of the approach 則 Future work Extend categories and vocabularies for detection Add more libraries for visual elements in visualizations Provide templates for generating mash-ups that combine domains Investigate the importance of a category within a dataset Provide a user evaluation 2014/10/20 #COLD2014 Riva del Garda, Italy - 17
Questions? http://ww.slideshare.net/ghislainatemezing/cold2014-ldvizwiz

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cold2014-ldvizwiz

  • 1. Towards a Linked-Data Visualization Wizard Ghislain A. Atemezing (@gatemezing)* Rapha谷l Troncy (@rtroncy) (*) The author thanks the Semantic Web Science Association (SWSA) for the grant receives to particiapte at ISWC, 2014.
  • 2. Goal and Agenda 則 Goal: Build a visualization wizard based on the RDF stack 則 Motivation Gap between traditional InfoVis tools and Semantic Web applications Graphs are not meant to be shown to end-users 則 Current situation Visualizations are built on known datasets and vocabularies what happen with unknown datasets and vocabularies? 則 Proposal: create generic visualizations based on data analysis of the RDF graphs 則 Conclusion and Perspectives 2014/10/20 #COLD2014 Riva del Garda, Italy - 2
  • 3. Motivation 則 Many structured datasets are now available on the Web (3 billions of Triples in the DBpedia 2014 release) 則 RDF is not what we show to end-users 則 InfoVis community has mature tools and studies on visualizing information 則 Triples are good but they need to be beautiful for end-users 則 In the era of structured big data, we also need tools for Webbased visual analysis and reporting 2014/10/20 #COLD2014 Riva del Garda, Italy - 3
  • 4. Challenges Dont 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. A Journey of a Web Application Developer 則 Scenario 1: Known Datasets, Known vocabularies Specific SPARQL queries Visualizations: dataset specific 則 Example Datasets on schools in France Vocabularies: geo vocab, data cube, geometry. Application: PerfectSchool 2014/10/20 #COLD2014 Riva del Garda, Italy - 5
  • 6. A Journey of a Web Application Developer 則 Scenario 2: Unknown Datasets, Known domains, so domain-specific SPARQL queries Visualizations: domain specific 則 Example Endpoints of geo datasets Domain: geospatial Application: GeoRDFviz 2014/10/20 #COLD2014 Riva del Garda, Italy - 6
  • 7. A Journey of a Web Application Developer 則 Scenario 3: Unknown Datasets, Unknown domains, so generic SPARQL queries Visualizations: adapted to domains specific 則 Example Any endpoints Multiple domains: geodata, statistics, persons, cross-domains, etc.. 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. Our Proposal Linked Data Vizualization Wizard (LDVizWiz) 2014/10/20 #COLD2014 Riva del Garda, Italy - 8
  • 9. Requirements of LDVizWiz (LDViz-Wise) 則 Predefined categories associated to visual elements 則 Build on top of RDF standards e.g. SPARQL queries; Semantic Web technologies 則 Reuse existing Visualization libraries e.g. Google Maps, Google Charts, D3.js, etc. 則 Input: Datasets published as LOD 則 Reuse Information Visualization Taxonomy 則 Target to non RDF/SPARQL speakers 2014/10/20 #COLD2014 Riva del Garda, Italy - 9
  • 10. Mapping Categories and vocabularies 則 Geographic information Geo, GeoSparql, etc. 則 Temporal information Time, interval ontologies 則 Event information lode, event, sport, etc. 則 Agent/Person foaf, org 則 Organization information ORG vocabulary, vcard 則 Statistics information Data cube, SDMX model 則 Knowledge information Schemas, classifications using SKOS vocabulary 2014/10/20 #COLD2014 Riva del Garda, Italy - 10
  • 11. LDVizWiz Workflow 2014/10/20 #COLD2014 Riva del Garda, Italy - 11
  • 12. Step 1: Categories detection 則 Detection of main categories in datasets ASK SPARQL queries on predefined categories Uses well-known vocabularies in LOV Unveil main facets of the visualizations 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. 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% 444 endpoints (*) analyzed, 278 good answers (62.61%) using ASK queries. Few taxonomies in SKOS, many GEO DATA 則 Applications Automatic detection of endpoints categories More trustable than human tagging 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. Step2: Properties Aggregation 則 Goal: Exploit the connectors between graphs 則 connectors are used to enrich a given graph e.g. owl:sameAs, rdfs:seeAlso, skos:exactMatch 則 Retrieve properties from external datasets So called enriched properties 則 Build candidate properties for visualization For pop-up menus For facet browsing For charts display 2014/10/20 #COLD2014 Riva del Garda, Italy - 14 Detection Aggregation
  • 15. Step3: Publication 則 Visualization Generator Recommend the visual elements based on categories Transform ASK queries to SELECT or CONSTRUCT queries for input to visual library 則 Visualization Publisher Export the description of a visualization in RDF Add metadata for the visualization (charts) and the steps used to create it e.g. dcat:Dataset, prov:wasDerivedFrom, void:ExampleResource, chart vocabulary 2014/10/20 #COLD2014 Riva del Garda, Italy - 15 Detection Aggregation Publication
  • 16. Current Implementation 則 Javascript light version as proof-of-concept 則 http://semantics.eurecom.fr/datalift/rdfViz/apps/ 2014/10/20 #COLD2014 Riva del Garda, Italy - 16
  • 17. Conclusion and Future Work 則 LDVizWiz: a tool to generate visualizations Based on RDF standards, target to lay-users for graph analysis Composed of 3 main steps: category detections, property aggregation and visualization publication 則 A Javascript implementation shows the usefulness of the approach 則 Future work Extend categories and vocabularies for detection Add more libraries for visual elements in visualizations Provide templates for generating mash-ups that combine domains Investigate the importance of a category within a dataset Provide a user evaluation 2014/10/20 #COLD2014 Riva del Garda, Italy - 17