Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.
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
Nächste SlideShare
Wird geladen in …5
×

cold2014-ldvizwiz

1.252 Aufrufe

Veröffentlicht am

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.

Veröffentlicht in: Wissenschaft
  • Login to see the comments

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

×