Linked Open Data Visualization
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The document outlines a training session focused on linked data visualization, emphasizing its significance in efficiently handling large datasets and improving user interaction. It covers practical tools and techniques for visualizing linked open data (LOD), including various system classifications, exploratory search systems, and examples of specific tools like lodex and lodlive. Additionally, it discusses the limitations and challenges faced in LOD visualization, such as tool prototypes and performance issues with SPARQL endpoints.
Linked Open Data Visualization
- 1. Linked Data Visualization 3th Keystone Training School - Keyword Search in Big Linked Data Institute for Software Technology and Interactive Systems, TU Wien, Austria Laura Po, PhD Department of Engineering "Enzo Ferrari" University of Modena and Reggio Emilia Italy
- 3. Goal of the Talk • To provide practical skills required for exploring LOD sources • The importance of visualization • How a Linked Data Visualization Process can be defined • Practical use of LOD/ RDF browsers and visualization toolkits Outline
- 4. Why is visualization of Linked Data important? - Large and Dynamic Data - Efficiently and effectively handle billions of objects within dynamic datsets - Visual presentation and interaction issues - Offer ways to easly explore datasets - Proposing summaries and overviews - Incremental and progressive techniques - Variety of Users and Tasks
- 5. BOLD – Big Open Linked Data "The bigger the number, the harder it can be to visualise" Bratsas et al (2016), Preface on special session “data impact: Big, open, linked data innovations” at 11th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) https://doi.org/10.1109/SMAP.2016.7753368 Dwivedi et al, (2017) Driving innovation through big open linked data (BOLD): Exploring antecedents using interpretive structural, Inf Syst Front (2017) 19:197–212 https://link.springer.com/article/10.1007/s10796-016-9675-5 Saxena, (2017) BOLD (Big and Open Linked Data): what’s next?, Library Hi Tech News, Vol. 34 Issue: 5, pp.10-13, https://doi.org/10.1108/LHTN-04-2017-0020 Craig, (2016), BOLD: The power and potential of Big Open Linked Data, Published on 11 Oct 2016 on the Thomson Reuters Blog https://blogs.thomsonreuters.com/answerson/bold-power-potential-big-open-linked-data/
- 6. Why visualize data instead of provide statistic analysis? http://en.wikipedia.org/wiki/Anscombe's_quartet • Anscombe's quartet of datasets having similar statistical properties but appearing very different when plotted
- 7. Users PRODUCERS Consumers domain expert Lay-users Technicalexpert
- 8. LOD Visualization • LOD simplifies accessing and integrating data from different sources • SPARQL makes it easy to select from, and analyse the data • It's natural to visualise the data as graphs (networks) … but other forms of visualisation also possible
- 9. RDF Graph Reference for the picture: http://www.obitko.com/tutorials/ontologies-semantic-web/rdf-graph-and-syntax.html
- 10. Example of LOD visualization process
- 11. Heatmap visualization of The Beatles releases
- 12. LOD visualization systems They can be classified in 6 categories 1. Browsers and Exploratory systems 2. Generic visualization systems 3. Domain vocabulary & device specific systems 4. Graph-based visualization systems 5. Ontology visualization systems 6. Visualization libraries Bikakis and Sellis, (2016) Exploration and Visualization in the Web of Big Linked Data: A Survey of the State of the Art. Proceedings of the Workshops of the EDBT/ICDT 2016 Joint Conference, EDBT/ICDT Workshops 2016, Bordeaux, France, March 15, 2016 http://ceur- ws.org/Vol-1558/paper28.pdf
- 13. Evolution over time Marie and Gandon, (2014) Survey of Linked Data Based Exploration Systems, Proceedings of the 3rd International Workshop on Intelligent Exploration of Semantic Data (IESD 2014) co-located with the 13th International Semantic Web Conference (ISWC 2014), Riva del Garda, Italy, October 20, 2014 http://ceur-ws.org/Vol-1279/iesd14_8.pdf
- 14. Exploratory search • Exploratory search systems (ESS) forms a special category of seeking information on the Web with the purpose of revealing related information to the searcher along with retrievals of what have been searched for. Palagi, et al. (2017), A Survey of Definitions and Models of Exploratory Search. Proceedings of the 2017 ACM Workshop on Exploratory Search and Interactive Data Analytics. ACM, http://doi.acm.org/10.1145/3038462.3038465 Marie,(2015) , Linked data based exploratory search. PhD Thesis, Université Nice Sophia, Antipolis, https://tel.archives-ouvertes.fr/tel-01130622
- 16. Hoefler, Patrick, et al. "Linked Data Query Wizard: A Novel Interface for Accessing SPARQL Endpoints." LDOW. 2014. http://code-research.eu/ EU project 2012-2014 https://code.know-center.tugraz.at/search
- 17. TEST • Using CODE Linked Data Query Wizard search for "Johann Strauss II" within the Dbpedia source • Explore the result • Add columns that show some property like "birth place", "given name", "music composer of ", ...
- 20. TEST • Navigate the Linked Jazz cloud • Change the visualization option (fized, similar, gender, dynamic)
- 21. LOD live LodLive project provides a demonstration of the use of Linked Data standards (RDF, SPARQL) to browse RDF resources. The application aims to spread linked data principles using a simple and friendly interface with reusable techniques. http://en.lodlive.it/ http://en.lodlive.it/?http://dbpedia.org/resource/Jules_Verne
- 23. TEST By using LodLive online to explore dbpedia resources, search for Johann Strauss II http://en.lodlive.it/ - who is he? - where was he born? where did he died? - Is he the son of Johann Strauss? - find which type are associated to him
- 25. LODmilla Micsik, András, Sándor Turbucz, and Zoltán Tóth. "Exploring publication metadata graphs with the LODmilla browser and editor." International Journal on Digital Libraries 16.1 (2015): 15-24. Micsik, András, Sándor Turbucz, and Zoltán Tóth. "Browsing and traversing linked data with lodmilla." ERCIM News 2014.96 (2014): 35-36. http://lodmilla.sztaki.hu/lodmilla
- 26. TEST Using LODMilla search and add the following node from Dbpedia: • Johann Strauss II • Vienna • The Blue Danube • Austria • Johann Strauss I • Wolfgang Amadeus Mozart • Composer • Musician • Look at the connections between nodes
- 27. LODEX It is a tool for producing a representative summary of a Linked open Data (LOD) source starting from scratch, thus supporting users in exploring and understanding the contents of a dataset. LODeX extracts statistical indexes that uses to build the representative summary, by quering the SPARQL endpoint of a LOD source. • LODeX 2.0 (http://www.dbgroup.unimo.it/lodex2 ) includes the possibility to compose visual queries by selecting objects from the representative summary of a LOD source • LODeX Cluster (http://www.dbgroup.unimo.it/lodex2/testCluster ) provides a more concise schema for huge datasets
- 28. LODeX Architecture LOD Cloud SPARQL Queries LODeX Post- processing Statistical Indexes LODeX Indexes Extraction Endpoint URLs Schema Summary NoSQL SPARQL Queries Schema Summary Query Orchestrator Schema Summary Visualizzation Basic QueryResults Benedetti, et al. (2015), Exposing the Underlying Schema of LOD Sources. 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) (IEEE) ISBN: 9781467396189 Benedetti, et al. (2015), Visual Querying LOD sources with LODeX. Proceedings of the 8th International Conference on Knowledge Capture (ACM) Benedetti, et al. (2015), LODeX: A tool for Visual Querying Linked Open Data. Proceedings of the ISWC 2015 Posters & Demonstrations Track @ (ISWC 2015), n. volume 1486 Benedetti, et al. (2014), A Visual Summary for Linked Open Data sources. ISWC 2014 Posters & Demo Track, Riva del Garda, Italy, ISSN: 1613-0073 Benedetti, et al. (2014), Online Index Extraction from Linked Open Data Sources. Second International Workshop on Linked Data for Information Extraction (LD4IE) @ (ISWC 2014), Riva del Garda, Italy, ISSN: 1613-0073
- 29. A running example ex:Sector foaf:Organization owl:Class ex:sector “sector” rdf:type rdf:type rdf:Propertyrdf:type owl:ObjectProperty rdf:type sector1 organization1ex:sector dc:title “Energy” Extensional Classes Extensional Knowledge Intensional Knowledge ex:activity “Village electrification in the Pacific” organization2 “+41331231” rdfs:label rdfs:label rdfs:domain rdf:type ex:sector rdf:type rdf:type dbpedia:fax person1 foaf:Person ex:activity “Paolo” rdf:type ex:ceo rdf:type foaf:firstName foaf:lastName “Rossi” The information contained in the Intensional knowledge can be incomplete or absent
- 30. Schema Summary – Building a Visual Query
- 31. Refinement Panel
- 32. TEST By using Lodex http://www.dbgroup.unimore.it/lodex2/ find, navigate and explore the following datasets: • European Television Heritage • How many classes it has? How many properties it has? • How many vocabulary are used? • Nobel Prizes • How many vocabulary are used? • Define a query that select person (label, gender,name) that won a Nobel Prize , i.e.have an Award (year,label), add also the Category of the Award if it exists
- 33. Conclusions • Analysis of the needs for visualization in the LOD context • Practical use of some LOD browsers and visualization toolkits • Navigation and exploration of some datasets and the construction of different visualizations
- 34. Actual limitations and challenges • Most of the LOD visualization tools are still in-lab prototypes • Lots tools allow the exploration of a limited list of datasets or have limitations in terms of size, format (SPARQL endpoint/RDF dumps) of the datasets they can explore • SPARQL endpoints might be offline or have bad performance such as taking long time to respond to some queris. • For dealing with BOLD, graph simplification is needed: • reducing size could be possible through filtering or aggregation
- 35. RDF Graph Aggregated View Schema Summary My vision
- 36. Feel free to contact me at [email protected] You can find more information on my research and my group at www.dbgroup.unimore.it Slide are available on http://www.slideshare.net/polaura