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Keynote at the MTSR conference

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Presentation I gave at MTSR 2016 in Goettingen

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Keynote at the MTSR conference

  1. 1. 18 years of engagement for metadata and semantics in Agriculture: was it worth the effort Dr. rer. nat. Johannes Keizer, Goettingen, 2016-11-24
  2. 2. What we achieved really • No one denies the importants of semantics anymore, Ontologies everywhere • Agroportal and GACS .. community not FAO alone • The VocBench (a nice piece of open Source software) • The AGRIS LOD application is a powerful demonstrator, what LOD can deliver • Other important experiements: A prototype of a dataset registry (CIARD RING), Agriprofiles • Conceptual framework • and most important!
  3. 3. http://aims.fao.org
  4. 4. Community
  5. 5. SEMANTICS ON THE WEB Looking around
  6. 6. The semantic web The 2001 Scientific American article by Berners-Lee, Hendler, and Lassila described an expected evolution of the existing Web to a Semantic Web.[5] In 2006, Berners-Lee and colleagues stated that: "This simple idea…remains largely unrealized".[6] In 2013, more than four million Web domains contained Semantic Web markup.[7] According to the Verisign Domain Name Industry Brief the total number of registered domain names is 284 million, an increase over the January 2014 figure of 265 million
  7. 7. BUT WHAT DOES IT MEAN IN PRACTICE? The cloud gets bigger
  8. 8. What application do you mostly use? • Evernote • Google Suite • Dropbox • Economist • Amazon • Drupal • Facebook
  9. 9. Semantics in Evernote Existent! But Kind of ridiculous • Simple tagging system • No polyhierarachy allowed (poly hierarchy is one of the most real events ) • No relations between tags • Useless inheritance features
  10. 10. Google Suite • The same charme as normal google search • Have you created and used categories in Gmail? • Why not even auto categorization? • «Add recipients» most useful feature • Google Drive still uses mostly the concept of physical folders • Most effort into displaying adds
  11. 11. Amazon The most intelligent app They mostly get it • Artificial Intelligence behind • Analyzing reading habits • But the possibilities would be enormous – Think about autocategorization and linking of your notes • But: closed shop
  12. 12. Dropbox
  13. 13. Drupal Drupal has modules that allow to : • expose internal data as RDF; • expose internal data through a SPARQL engine; • dynamically query remote RDF stores through a SPARQL client; • execute dynamic SPARQL queries and store resulting triples as nodes according to a pre- defined mapping  Linked Data
  14. 14. Economist
  15. 15. Facebook
  16. 16. EXCEPT IN THE CASE OF DRUPAL THE SEMANTIC WEB COMMUNITY HAS NOT REALLY INFLUENCED MAIN STREAM APPLICATIONS Conclusions
  17. 17. BUSINESS CASE NOT STRONG ENOUGH? Why?
  18. 18. “… research suggests that seven sectors alone could generate more than $3 trillion a year in additional value as a result of open data, which is already giving rise to hundreds of entrepreneurial businesses and helping established companies to segment markets …” Source: McKinsey Global Institute "Making these data public will allow people to make their own assessments of the progress of our Good Growth Plan. It is also blurring the traditional roles of business, government and NGOs by highlighting our collective responsibility to address acute global challenges. Above all, the data will be of value to farmers, enabling them to increase productivity sustainably and to enhance their livelihoods." "Open data has the power to solve our most challenging sustainability problems. … Agri- tech businesses have a big role to play in finding novel solutions to these problems. … Syngenta is taking a step that puts them at the forefront of the open data movement in their sector. We look forward to working with them to unlock benefits for farmers and consumers worldwide." Mike Mack, CEO of Syngenta (2015, for 1st GGP data release) Jeni Tennison, Deputy CEO and CTO of the Open Data Institute Now there is big data Hype
  19. 19. Paradigm Shift for research • In the past – 80% data production, 20% data evaluatio • In the future – 20% data production, 80% data evaluation
  20. 20. We have GODAN • Advocacy • Think Tank • Knowledge Network
  21. 21. Aggregation States of Knowledge
  22. 22. Data and Information in Agricultural Research and Extension
  23. 23. Dataset Registries Data processingSemantics LOD Generator triplifier, concept and entity identifier Data Services Webservices + APIs to data stores Data Storage Infrastructure elements for FAIR data
  24. 24. THE FAIR PRINCIPLE resources need to be Findable Accessible Interoperable Reusable = FAIR FAIR principle by Barend Mons, EC, EOSC
  25. 25. Data Issues http://www.nature.com/ng/journal/v43/n4/full/ng0411-281.html
  26. 26. Going on! • Semantics are needed • Community Building is Prime necessity- Chania Group • Pragmatism in approach, avoiding over commitment • Developing plans for a data infrastructure • Riding the big data tiger • Influencing mainstream productivity tools
  27. 27. eRosa

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