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Smart organization of agricultural knowledge: the example of the AGROVOC Concept Server

Smart organization of agricultural knowledge: the example of the AGROVOC Concept Server and Agropedia. ISKO Italy Open conference systems,  Paradigms and conceptual systems in KO Roma, 24 February 2010. Few words about myself. Outline. Why such projects The AGROVOC Concept Server

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Smart organization of agricultural knowledge: the example of the AGROVOC Concept Server

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  1. Smart organization of agricultural knowledge: the example of the AGROVOC Concept Server and Agropedia ISKO Italy Open conference systems, Paradigms and conceptual systems in KO Roma, 24 February 2010

  2. Few words about myself

  3. Outline • Why such projects • The AGROVOC Concept Server • Benefits • Technology • The Agropedia Project • Benefits • Technology • Conclusion

  4. Why such projects? • Adding semantics to Agricultural Knowledge • Agricultural Ontology Service • Scope • Better define and describe knowledge • Give meaning and structure to information • Enable reuse of domain knowledge • Avoid ambiguities • Allow better searches • Provide smart services • …

  5. The starting idea… • Semantic technologies were evolving • Ontologies • Concepts • URIs • Machine readable formats • Everything started from AGROVOC… • Multi-lingual • Multi-domains • Re-engineering

  6. Architecture of AOS ontologies Foundational Layer Lexicalizations Foundational Agricultural Ontology imports imports Domain Specific Layer Rice Ontology Pest Ontology Agricultural Domain Specific Ontology Plant Ontology imports Application Specific Layer Indian Rice Ontology Rice Cultivation Ontology Application Specific Ontology

  7. AGROVOC Concept Server

  8. AGROVOC Concept Server • A knowledge base of Agricultural related concepts organized in ontological relationships (hierarchical, associative, equivalence) • Will contain 600.000 terms in around 20 languages • Concepts can be organized in multiple categories

  9. AGROVOCRDFS formats (e.g. SKOS) and TagTextISO2709 TerminologyWorkbench AGROVOCOWL AOS Core: the Concept Server ABACA NT1 Food NT2 Apple ANIMAL BT Organ NT .... Other thesauriandterminologies integration Other thesauri & terminologies mapping Export ABACA NT1 Food NT2 Apple ANIMAL BT Organ NT ....

  10. Three levels of representation • Concepts (the abstract meaning) • Ex: ‘rice’ in the sense of a plant, • Terms (language-specific lexical forms) • Ex: ‘Rice’, ‘Riz’, ‘Arroz’, ‘稻米’, or ‘Paddy’ • Term variants (the range of forms that can occur for each term) • Ex: ‘O. sativa’ or ‘Oryza Sativa’, ‘Organization’ or Organisation’

  11. Concept example • Organization • hasLexicalization • Organizações (pt) • Organization (en) [P. T] hasSpellingVariant • Organisation (uk-en) • hasSubClass department (en) • hasStatus • Published • hasDateCreated • 12/12/2006 • hasDateUpdated • 01/10/2009

  12. Semantic Relationships

  13. Current AGROVOC MySQL AGROVOC OWL Revision and Refinement Improved AGROVOC MySQL Towards the Concept Server • AGROVOC cleaning and refinement

  14. concept level Relationships between Relationships Relationships between concepts Concept Relationship annotation relationship designated by Relationships between terms string level Lexicalization/ Term term level All terms are created as instances of the class o_terms. All at the same level. Only one language per term. Note Other information: language/culture subvocabulary/scope audience type, etc. manifested as Relationships between strings String Ontology models (AGROVOC Concept Server, LIR, ...)

  15. The Workbench • A web-based working environment for managing the AGROVOC Concept Server • Facilitate the collaborative editing of multilingual terminology and semantic concept information • It includes administration and group management features • It includes workflows for maintenance, validation and quality assurance of the data pool

  16. Users/Roles/Groups • Non registered users • Term editors • Ontology editors • Validators • Publishers • Administrators

  17. Modules • Home • Search • Concept/Term Management • Relationship Management • Classification Scheme Management • Validation • Consistency Check • Import/Export • User/Group Management • Statistics/Preferences

  18. Concept/Term Management

  19. Concept Relationship • Can create the concept-concept relationship • Inverse relationship is also created automatically • Ex: If we create A affects B, then B isAffectedBy A relationship is also created

  20. Graphical Visualization

  21. Term Relationship • Add/edit/delete term-term relationship • Relationships can be • is scientific name of • has scientific name • has synonym • has translation • is acronym of • has acronym • has abbreviation

  22. Term Spelling Variant • Can assign the different spelling variant for the terms in different languages • Ex: • color (us-en) • colour (uk-en)

  23. Classification Schemes

  24. RSS

  25. Web services

  26. System Architecture (1/2) • Triple store database (MySQL and sesame) • System database (MySQL) • AJAX technology (Google Web Toolkit) • Java • Queries to the triple store using SEMRQL • Organized in modules

  27. AGROVOC WORKBENCH CONCEPT SERVER INTERFACE GWT User Management Group Management System Preference Statistics Consistency Check Import Export Search Scheme Management Concept Management Relationship Management Validation JDBC (MYSQL) Protégé OWL API System Data Repository Ontology repository (OWL) System Architecture (2/2)

  28. Benefits • Agricultural related concepts will be uniquely identified • URI-based indexing and search systems • Multiple terms in many languages (include spelling variants, acronyms, dialectal forms or local terms used in specific geographical area) • freedom to use any language • Ability of creating catalogues more machine-interpretable; • More interoperability with other systems using ontologies • mapping and linking to other URI

  29. Agropedia

  30. this is a document about rice and its pests..... Once the rice ap- pear in the world ..... Mad Cow Disea- se is the commonly used name for Bovine Spongiform Encephalopathy (BSE) .... Rice Saket-4 Telegu Spanish Hindi English What is Agropedia Indica Knowledge Repository on Agriculture Of universal knowledge models And localized content For a variety of users With appropriate interfaces Built in collaborative mode In multiple languages

  31. Scope • Build an infrastructure of agricultural knowledge • Multilingual and localized information • Knowledge Models (KMs) as conceptual reference • Different crops (Chickpea, Groundnut, Litchi, Pigeon pea, Rice, Sorghum, Sugarcane, Vegetable pea, and Wheat) • Domain specific information (local fertilizers, soil, cropping techniques and methods, …) • Present it in various ways • Different stakeholders: scientists, students, extension workers, farmers, policy makers, agronomists, soil scientists, plant breeders or geneticists, farm managers, and other experts • Specific guidelines • Registry of relationships (object properties and data type properties)

  32. METADATA author: ... subject: .... identifier: .... author: ... subject: .... identifier: .... author: ... subject: .... identifier: .... author: ... subject: .... identifier: .... this is a document about rice and its pests..... Once the rice ap- pear in the world ..... Mad Cow Disea- se is the commonly used name for Bovine Spongiform Encephalopathy (BSE) .... wav, audio, ... docs, pdf, txt, ... htm, html, asp, php, ... jpg, gif, bmp, ... Knowledge Objects URI

  33. this is a document about rice and its pests..... Once the rice ap- pear in the world ..... Mad Cow Disea- se is the commonly used name for Bovine Spongiform Encephalopathy (BSE) .... Retrieval results.....

  34. Services • Navigate knowledge maps • Concept indexing • Blogs (experts can create blog on specifics topics and farmers can post questions and comments) • Q/A forum • FAQ • Agrowiki (a common platform where everyone can share experiences) • Multilingual services

  35. Knowledge base structure • Agricultural Experts can upload content as: • crops calendar • publications (journals, articles, magazines, thesis, books) • do’s and don'ts (extension knowledge) • sponsor content • ... • Content (except agrowiki) will be verified by experts • Agricultural related issues in Agrowiki

  36. Conceptual Architecture User requests Interface Layer Knowledge model server Semantic Layer Upload view Content Resource Layer Digital Objects

  37. Technology • First release implemented using Alfresco • Subsequently, because of the need of incorporating other functionalities, Drupal • blogs, chats, forums, Q/A, user management, etc. • Cmap for the KMs, and exported in SVG format • Other formats (pdf, jpg) for visualization only • Java to customize the OWL version of the Kms • Taxonomy module for tagging and searching the content • A Java module for automatic tagging using an the KMs is in process of implementation.

  38. Technical infrastructure Agropedia Indica Application UI User management Msql Upload view Content User requests

  39. Knowledge Models • A knowledge model is a function of its use • For the same domain one needs multiple models depending on the use/user • Researchers needed to identify these different models and build them • Consistent and coherent

  40. KM in Agropedia and AOS AGROVOC Agropedia KMs 70% 30% 16% 16% of all concepts in Agropedia KM are scientific names or common names

  41. Multilinguality ENGLISH Generic model Specific models translate AGROVOC Concept Server (via WS) HINDI TELUGU .... Generic model (Specific models from IITK)

  42. Innovative aspects • Agropedia presents to users different semantically oriented tools: textual and audio blogs, wikis, forums, and the KMs presented in different formats (pdf, static or context-sensitive images) • Users have the possibility to choose a preferred way of navigating the KMs • Resources from the library catalogue are tagged with concepts from the KM • No matter what languages the maps are displayed, the results will be always the same (currently, KMs exists in English and Hindi)

  43. Home News FAQ Kisan Blog About Sponsors NAIP ICAR has Services Who are you? Agro-scientist Extension worker Call Center Operator has Sponsors has Users Partners IITK IITB ICRISAT FAO GB PANT ..... Agropedia has Partners • What are you interestedtoday? • FAQ • Pesticides • Rice • Seasonal info • Agroclimatic zones • .... has content ..... ..... .....

  44. Knowledge Models in Agropedia • Crop • Pesticides • Rice • Rice pests • Rice diseases • ... many others

  45. Crop

  46. Rice cropping system

  47. Rice pests

  48. Rice diseases (detail)

  49. Insecticides (detail)

  50. Relationships concept-to-conceptand instance-to-instance

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