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Towards Ontology based Agricultural Knowledge Services

Towards Ontology based Agricultural Knowledge Services. Asanee Kawtrakul At FAO, Italy 21 September 2007. National Electronic and Computer Technology Center National Science and Technology Development Agency. Outline. Why need Agricultural Knowledge Service?

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Towards Ontology based Agricultural Knowledge Services

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  1. Towards Ontology based Agricultural Knowledge Services Asanee Kawtrakul At FAO, Italy 21 September 2007

  2. National Electronic and Computer Technology Center National Science and Technology Development Agency

  3. Outline • Why need Agricultural Knowledge Service? • Who are the target of the Services? • Three steps for providing the knowledge service • Q&A system:Case Studies on Rice. • Conclusion: Current and Next Steps

  4. Why need Agricutural Knowledge Services? • Information Scattered • Information Overload Solutions: Assumption: Knowledge derived from Appropriate Information • Right Information (Necessary and Sufficient) for Right Purpose • Right Information Representation for the good Imaginary

  5. Who are the Target of Knowledge services? Policy Decision Maker Related area Researchers Domain Specific Researchers,Knowledge Broker Farmer, Naïve People, SME- Business

  6. Three steps for providing the knowledge service • Ontology Base Construction and Maintenance with Good Acquisition Tool or Workbench • Content Management with Good Browser • Knowledge Service with Good Devices

  7. Communities Authoring Tools GUI for Ontology Acquisition & Maintenance User Management Ontological Knowledge Management Morphological Analysis and Phrase chunking Task-Oriented Parsing Lexicon Information Integration Ontology Extraction Filtering & Correcting Ontology Integration Raw Text MRD Text Validation Sesame API JDBC API SQL SeRQL System Data Repository Ontology Repository in OWL format (MySQL) Printed Dictionaries User management System Statistic Report Group management System preference Consistency check Import Export Search Scheme management Concept management Relationship management Ontological Knowledge Authoring Tools User management Group management System preference System Statistic Report System management module Structured Corpus Thesaurus Dictionary Morphological Analysis Structure Analysis Taxonomic, Part-of, Synonym Relationship Acquisition by using Pattern Relationship Refinement Relationship Acquisition (Phrase Level) Non-Taxonomic Relationship Acquisition (Sentence Level) Merging & Organizing VerificationSystem Ontology

  8. Processed plant products Paddy Luang Patiw Jasmine rice Tung kula ronghai An Example of Rice Ontology Merging Plant products Cereals Maize Rice Jasmine rice Khao Dore Luang Patiw Jasmine rice 105 ko-kho15

  9. Towards Knowledge management with good Content management andKnowledge Service Tool

  10. Ontology Acquisition Task Oriented Ontology & Linguistic Knowledge WWW Distributed Information Collection Knowledge Portal Construction Knowledge Service Provision Farmers Pest Rice Variety Meta Data Information Extraction & Integration Inference Engine Business Query Processing Preriodic web crawler Knowledge Extraction & generalization Organization Based Knowledge Tracking Harvest Technology Researchers Disease Rice Variety Document Warehouse Structured Knowledge & rules Government

  11. Q & A SystemDifferent Answer for Different Need

  12. For Knowledge Broker Economic Plants -> Rice and Sugar Types of rice -> Jasmine and Basmati Jasmine -> 2 types Jasmine 105 and Jasmine 192 Jasmine 105 -> can have Pest 1 and Pest 2 Pest 1 -> can be controlled using Method 1 and Method 2 Pest 2 -> can be controlled using Method 3

  13. For Researchers Research Organization Kasetsart University NII FAO Author 1 Author 4 Author 5 Author8 Author 9 • D7.rdf • D8.xml • D14.doc • D15.txt • D16.pdf • D1.pdf • D2.htm • D3.doc • D4.doc • D5.pdf • D6.pdf • D29 • D30 • D31 • D32 • D33 • D34 Author 3 • D35 • D36 • D37 • D38 • D39 • D40 • D41 • D42 • D43 Author 6 • D17.pdf • D18.pdf • D19.jpg • D20.jpg • D21.rdf • D22.xtm • D23.xml • D24.pdf • D25.pdf • D26.doc • D27.doc • D28.doc Author 2 Author 7 • D9.xls • D10.htm • D11.htm • D12.htm • D13.ps • D35.sql • D36.xml Author 10 • D45.ppt

  14. for the Farmers Question: What method can be used to remove pest [Pest Name 1] for rice [Jasmine 105] ? Answer: • Method 1 • Method 2 Rice Name: Jasmine 105 Pest Name: Pest 1 Search

  15. K-service for the Exporter/SME Where does the upland rice grow in? Know Where Southern Region

  16. K-service for General People What kind of rice enrichwith Vitamin B1 for beriberi disease protecting? Know What 1. Hom Mali (Jasmine rice) 2. Sang Yod (ข้าวสังข์หยด)

  17. What kind of rice that resist Sheath Rot Disease and Ragged Stunt Disease K-service for the Farmers Phatumthanee 60 and Supanburi 90

  18. Conclusion: Current and Next Steps: • Ontology Workbench Implementation with the experts on rice • Do detail in End-users requirement (Q&A) • Develop Knowledge Portal about Rice • Develop the K-Service (Know-how, Know-why, Know-who, Know-what • Evaluate and Test by different end users

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