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Syrtaki - ESWC SSchool 14 - Student project

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SyrtAPI is a new entertainment platform that combines music and book data from multiple sources like MusicBrainz and NYTimes reviews. It uses Linked Data and SPARQL queries to extract lyrics and reviews and recommend songs that match the content of the books. The team learned about using Linked Data vocabularies and linking datasets while building the prototype, which currently retrieves 25 lyrics and 10 book reviews through its pipeline. Future work includes adding more data sources, developing a mobile app, and using natural language processing to better analyze texts.

Science
1 of 18
Surtaki - Syrtaki Group n. 7: Ignacio Traverso, Elena González-Blanco, Sebastien Ballesteros, Abduladem Aljamel
The idea… Many people like listening to music while they read books…
The idea… But sometimes is not to easy to match your favorite music with the content of the book you are reading!!!
But we offer a
What is SyrtAPI? It’s a new way of entertainment
What is SyrtAPI? It’s a new way of entertainment It saves time
What is SyrtAPI?  It is a linked data based application  It combines different datasets  www.musicbrainz.org  http://www.geonames.org  http://dbpedia.org
What is SyrtAPI?  With cool stuff!  Music lyrics http://lyrics.wikia.com  And book reviews NYTimes Reviews
How to cope with this …?? Syrtaki team, 4 september 2014, 16:00 …
But finally inspiration arrived… Syrtaki team, 4 september 2014, 19:00 …
SPARQL Musicbrainz Dataset
SyrtAPI Architecture Wikilyrics HTML NYTimes Bestseller JSON NLP Module CONVERTER JSON LD Recommended URI songs Music Brainz (RDF) BoilerPipe Lyric Extranctor Harvester SPARQL COUCH – DB (JSON) JS DOM Client #LD JS Client #LD JS Client #LD JS
Conclusions & Results  Always bet on JavaScript & JSON LD  Cool stuff like Linked Data Fragments  Full SPARQL query client side is 6 lines of JS!  It’s good to combine classical restful API, hypermedia and Linked Data
Conclusions & Results  So far we got  Pipeline ready waiting for internet connection!  25 lyrics  10 books reviews  a cool website
Lessons Learned  We’ve learned a lot! … not only SPARQL, but also  How to use Linked Data vocabularies  How to link Linked Data datasets  The difficulty of finding data  Teamworking  Other Cool technologies
Future work…  Adding more datasets  Adding more and different sources of book reviews  Developing a mobile app html 5  Link our app to music libraries like iTunes  Add more NLP to anaylse the texts  Make money

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Syrtaki - ESWC SSchool 14 - Student project

  • 1. Surtaki - Syrtaki Group n. 7: Ignacio Traverso, Elena González-Blanco, Sebastien Ballesteros, Abduladem Aljamel
  • 2. The idea… Many people like listening to music while they read books…
  • 3. The idea… But sometimes is not to easy to match your favorite music with the content of the book you are reading!!!
  • 5. What is SyrtAPI? It’s a new way of entertainment
  • 6. What is SyrtAPI? It’s a new way of entertainment It saves time
  • 7. What is SyrtAPI?  It is a linked data based application  It combines different datasets  www.musicbrainz.org  http://www.geonames.org  http://dbpedia.org
  • 8. What is SyrtAPI?  With cool stuff!  Music lyrics http://lyrics.wikia.com  And book reviews NYTimes Reviews
  • 9. How to cope with this …?? Syrtaki team, 4 september 2014, 16:00 …
  • 10. But finally inspiration arrived… Syrtaki team, 4 september 2014, 19:00 …
  • 12. SyrtAPI Architecture Wikilyrics HTML NYTimes Bestseller JSON NLP Module CONVERTER JSON LD Recommended URI songs Music Brainz (RDF) BoilerPipe Lyric Extranctor Harvester SPARQL COUCH – DB (JSON) JS DOM Client #LD JS Client #LD JS Client #LD JS
  • 13. Conclusions & Results  Always bet on JavaScript & JSON LD  Cool stuff like Linked Data Fragments  Full SPARQL query client side is 6 lines of JS!  It’s good to combine classical restful API, hypermedia and Linked Data
  • 14. Conclusions & Results  So far we got  Pipeline ready waiting for internet connection!  25 lyrics  10 books reviews  a cool website
  • 15.
  • 16. Lessons Learned  We’ve learned a lot! … not only SPARQL, but also  How to use Linked Data vocabularies  How to link Linked Data datasets  The difficulty of finding data  Teamworking  Other Cool technologies
  • 17.
  • 18. Future work…  Adding more datasets  Adding more and different sources of book reviews  Developing a mobile app html 5  Link our app to music libraries like iTunes  Add more NLP to anaylse the texts  Make money