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Demo: Profiling & Exploration of Linked Open Data


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Talk at Open Data Seminar in St Petersburg, March 2014

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Demo: Profiling & Exploration of Linked Open Data

  1. 1. Motivation Data on the Web Some eyecatching opener illustrating growth and or diversity of web data Profiling & Exploration of Linked Datasets Stefan Dietze, Besnik Fetahu, Davide Taibi L3S Research Center, DE, @stefandietze, Stefan Dietze 12/03/14
  2. 2. Data curation and dataset profiling LinkedUp Dataset Catalog Stefan Dietze 12/03/14  Catalog of data (LinkedUp Catalog): classification of datasets according to resource types, disciplines/topics, data quality, accessability, etc  Infrastructure for distributed/federated querying describes  Which datasets are useful & trustworthy for case XY (eg „learning about the solar system“) ?  Which topics (eg „Astronomy“) are covered by dataset X?  Which datasets offer videos (slides, publications, statistics etc)?
  3. 3. LinkedUp Data Catalog in a nutshell  RDF (VoID) dataset catalog: browse & query distributed datasets  Live information about endpoint accessibility  Federated queries using type mappings Stefan Dietze 12/03/14
  4. 4. db:Astro. Objects Extracting Topic Profiles of Linked Datasets Dataset Metadata Stefan Dietze 12/03/14 Schema mappings BIBO AAISO FOAF contains Entity disambiguation Topic profile extraction db:Astronomy db:Astro. Objects LinkedUp Dataset Catalog yov:Video po:Programme BBC Programme <po:Programme …> <po:Series>Wonders of the Solar System</.> <po:Actor>Brian Cox</…> </po:Programme…> <yo:Video …> <dc:title>Pluto & the Dwarf Planets</dc:title> … </yo:Video…> Yovisto Video bibo:Fil bibo:Fi bibo:Film
  5. 5. What’s all the data about: exploring topics of LOD in a nutshell Stefan Dietze 12/03/14  Visualisation & exploration of dataset-topic-graph (datasets, topics, relationships)  Includes all responsive datasets of LOD Cloud A Scalable Approach for Efficiently Generating Structured Dataset Topic Profiles, Fetahu, B., Dietze, S., Nunes, B.P., Cassanova M., Nejdl, W. (2014), 11th Extended Semantic Web Conference, ESWC2014, Greece, May 2014.
  6. 6. Stefan Dietze 12/03/14 dbp:Category:Royal_Medal_winners dbp:Category:1955_births dbp:Category:People_from_London dbp:Category:Buzzwords dbp:Category:Web_Services dbp:Category:HTTP dbp:Category:Unitarian_Universalists dbp:Category:World_Wide_Web What have these categories in common?
  7. 7. Stefan Dietze 12/03/14 Diversity of category profile for a single paper Berners-Lee, Tim; Hendler, James, Ora Lassila (2001). "The Semantic Web". Scientific American Magazine. person document dbp:Tim_Berners-Lee dbp:Category:1955_births dbp:Category:People_from_London dbp:Category:Buzzwords dbp:Semantic_Web dbp:Category:Semantic_Web dbp:Category:Web_Services dbp:Category:HTTP dbp:Category:Unitarian_Universalists first-level categories (dcterms:subject) dbp:Category:World_Wide_Web dbp:Category:Royal_Medal_winners
  8. 8.  DBpedia category graph not an ideal “topic” vocabulary:  Broad and noisy  “Categories” vs “topics” (for capturing disciplines, thesauri like UNESCO Thesaurus seem better suited)  Lack of clear hierarchy: graph, not a tree  Mixing categories across resource types (document, person etc) creates “perceived noise”  But: broadness is useful as general vocabulary for categorisation of all sorts of resource types Stefan Dietze 12/03/14 Dataset profiling: some lessons learned
  9. 9. Stefan Dietze 12/03/14  Type specific views on datasets/ categories (ie “topics”)  “Document” (foaf:document)  “Person “ (foaf:person)  “Course” (aaiso:course)  Currently applied to datasets in LinkedUp Catalog only (as schema mappings already available here) More precise profiles of educational datasets
  10. 10. Thank you! 12/03/14 10Stefan Dietze