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The Roots: Linked data and the foundations of successful Agriculture Data

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The Roots: Linked data and the foundations of successful Agriculture Data

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Some thoughts on successful data for the agricultural domain. Keynote at Linked Open Data in Agriculture
MACS-G20 Workshop in Berlin, September 27th and 28th, 2017 https://www.ktbl.de/inhalte/themen/ueber-uns/projekte/macs-g20-loda/lod/

Some thoughts on successful data for the agricultural domain. Keynote at Linked Open Data in Agriculture
MACS-G20 Workshop in Berlin, September 27th and 28th, 2017 https://www.ktbl.de/inhalte/themen/ueber-uns/projekte/macs-g20-loda/lod/

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The Roots: Linked data and the foundations of successful Agriculture Data

  1. 1. THE ROOTS LINKED DATA AND THE FOUNDATIONS OF SUCCESSFUL AGRICULUTURE DATA Dr. Paul Groth | @pgroth | pgroth.com Disruptive Technology Director Elsevier Labs | @elsevierlabs G20 Workshop Linked Open Data and Agriculture September 27, 2017
  2. 2. QUESTIONS FOR THIS WORKSHOP 1. How can Linked Open Data make a difference in agriculture? 2. What technical obstacles stand in the way? 3. What policies are needed to achieve the potential?
  3. 3. DATA IS CENTRAL IN PRECISION AGRICULTURE Fig. 2 Precision agriculture information flow in crop production [after (19), modified]. Robin Gebbers, and Viacheslav I. Adamchuk Science 2010;327:828-831 Published by AAAS
  4. 4. THE DATA SUPPLY CHAIN IN AGRICULTURE Sjaak Wolfert, Lan Ge, Cor Verdouw, Marc-Jeroen Bogaardt, Big Data in Smart Farming – A review, In Agricultural Systems, Volume 153, 2017, Pages 69-80, ISSN 0308-521X, https://doi.org/10.1016/j.agsy.2017.01.023.
  5. 5. WHERE LINKED DATA CAN HELP Sjaak Wolfert, Lan Ge, Cor Verdouw, Marc-Jeroen Bogaardt, Big Data in Smart Farming – A review, In Agricultural Systems, Volume 153, 2017, Pages 69-80, ISSN 0308-521X, https://doi.org/10.1016/j.agsy.2017.01.023.
  6. 6. STARTING FROM THE GROUND UP
  7. 7. FAIR EVERYWHERE
  8. 8. CREATING SUCCESSFUL DATA
  9. 9. ENCOURAGING THE RESEARCHER
  10. 10. HOW DO RESEARCHERS SEARCH FOR DATA? Gregory, K., Groth, P., Cousijn, H., Scharnhorst, A., & Wyatt, S. (2017). Searching Data: A Review of Observational Data Retrieval Practices. arXiv preprint arXiv:1707.06937. Some observations from @gregory_km survey: 1. The needs and behaviours of specific user groups (e.g. early career researchers, policy makers, students) are not well documented. 2. Background uses of observational data are better documented than foreground uses. 3. Reconstructing data tables from journal articles, using general search engines, and making direct data requests are common.
  11. 11. DATA SEARCH Antony Scerri, John Kuriakose, Amit Ajit Deshmane, Mark Stanger, Peter Cotroneo, Rebekah Moore, Raj Naik, Anita de Waard; Elsevier’s approach to the bioCADDIE 2016 Dataset Retrieval Challenge, Database, Volume 2017, 1 January 2017, bax056, https://doi.org/10.1093/database/bax056
  12. 12. ENABLING DATASET DISCOVERY
  13. 13. INTEROPERABILITY & INTEGRATION
  14. 14. MOVING UP THE STACK
  15. 15. INTEGRATION
  16. 16. INTEGRATION ACROSS DOMAINS Entity recognitionDictionaries ConceptScan IE Patterns Grammar Pattern matching Processing PS Mammal Protein interaction facts ChemEffect ® Drug Effects DiseaseFxTM Disease State PS Plant Interactions in PlantsCartridges Pathway Studio technology overview Internal Documents Subscribed Titles* 116,125 full- text article Open Access journals 286,867 abstracts Plant Pathway ChemEffect® DiseaseFx™ Agrochemicals safety MaizeRice Rice Rice proteins and processes Maize Proteins and processes Pathway Studio Plant Knowledgebase >778,99 mln unique relations supported by >576,083 references § Automatically curated MedScan data § Compressed and purified by automatic curation ü removes historical redundancy (>30%) ü removes false positives (~5%) § Entity annotation § Entrez Gene for proteins § Pubchem for chemicals § Aliases from MedScan dictionaries § Protein functional annotation from Gene Ontology § Ontologies § Pathway Studio Ontology of intracellular signaling § Gene Ontology § Curated pathways § Cell signaling pathways § Metabolic pathways (AraCyc) 5 Before automatic curator After automatic curator
  17. 17. DATA SUSTAINABILITY
  18. 18. THINGS TO THINK ABOUT
  19. 19. ARE WE MISSING A USER?
  20. 20. WHAT CAN MACHINE INTELLIGENCE DO TODAY? If there’s a task that a normal person can do with less than one second of thinking, there’s a very good chance we can automate it with deep learning. Andrew Ng, Chief Scientist, Baidu (lecture at Bay Area Deep Learning School, Stanford, CA, September 24, 2016)
  21. 21. IMAGE RECOGNITION https://devblogs.nvidia.com/parallelforall/author/czhang/
  22. 22. ADVANCES ARE ENABLED BY MACHINE LEARNING input output algorithm input output model learning architecture data Programming Machine learning GPU CPU CPU
  23. 23. THESE RESULTS ARE DRIVEN BY DATA “The paradigm shift of the ImageNet thinking is that while a lot of people are paying attention to models, let’s pay attention to data, …” – Prof. Fei-Fei Li [1] [1] The data that transformed AI research—and possibly the world https://qz.com/1034972/the-data-that-changed-the-direction-of-ai-research-and- possibly-the-world/
  24. 24. RAW DATA From: Alain, G. and Bengio, Y. (2016). Understanding intermediate layers using linear classifier probes. arXiv:1610.01644v1.
  25. 25. VOCABULARIES ARE SETS OF VECTOR EMBEDDINGS From: Eisner, B., Rocktäschel, T., Augenstein, I., Bošnjak, M. and Riedel, S. (2016). Emoji2vec: learning emoji representations from their description. arXiv:1609.08359v1.
  26. 26. MODELS AS REUSABLE COMPONENTS Check out: sujitpal.blogspot.com for more
  27. 27. LINKED DATA & MACHINE LEARNING • Machines’ proficiency in learning to answer questions from text, audio, images and video will depend on our ability to train them effectively to read information from the Web • How machines read the Web today • Crawling and indexing Web resources, possibly semantically tagged (e.g. using schema.org) • Find-and-follow crawling of open linked data resources for ontology and data sharing and reuse • Programmatic access to APIs mediated through HTTP/S and other Internet protocols • Need to think about supporting ML oriented data
  28. 28. PROVENANCE FOR DATA Credits: Curt Tilmes, Peter Fox Tilmes, C.; Fox, P.; Ma, X.; McGuinness, D.L.; Privette, A.P.; Smith, A.; Waple, A.; Zednik, S.; Zheng, J.G., "Provenance Representation for the National Climate Assessment in the Global Change Information System," Geoscience and Remote Sensing, IEEE Transactions on , vol.51, no.11, pp.5160,5168, Nov. 2013
  29. 29. NATIONAL CLIMATE CHANGE ASSESSMENT PROVENANCE
  30. 30. FAIR TRADE + FAIR TRADE DATA? Groth, Paul, "Transparency and Reliability in the Data Supply Chain," Internet Computing, IEEE, vol.17, no.2, pp.69,71, March- April 2013 doi: 10.1109/MIC.2013.41
  31. 31. GOAL: SUCCESSFUL FAIR AGRICULTURE DATA 1. How can Linked Open Data make a difference in agriculture? 2. What technical obstacles stand in the way? 3. What policies are needed to achieve the potential?
  32. 32. THANK YOU Dr. Paul Groth | @pgroth | pgroth.com labs.elsevier.com

Editor's Notes

  • “ President and CEO Henry Huntington leads U.S. Department of Agriculture (USDA) Secretary Sonny Perdue on a tour of the precision controlled hydroponic system, and sees that in the time it takes for a gutter of baby greens to travel from one end of a green house to the other, plants will grow from seed to a harvestable size, in Loudon, NH, on Sept. 1, 2017. Also in the tour is New Hampshire Agriculture Commissioner Lorraine Stuart Merrill. USDA Photo by Lance Cheung.”
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