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ECOTERM Berlin, April 2005 GILW/FAO Margherita.Sini@fao

The Agricultural Ontology Service: a FAO initiative for more coherence in Agricultural Information Systems. ECOTERM Berlin, April 2005 GILW/FAO Margherita.Sini@fao.org. Outline. Why AOS AGROVOC Concept Server Phase 1 Phase 2 Metadata Schemas and Ontologies

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ECOTERM Berlin, April 2005 GILW/FAO Margherita.Sini@fao

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  1. The Agricultural Ontology Service: a FAO initiative for more coherence in Agricultural Information Systems ECOTERM Berlin, April 2005 GILW/FAO Margherita.Sini@fao.org

  2. Outline • Why AOS • AGROVOC Concept Server • Phase 1 • Phase 2 • Metadata Schemas and Ontologies • The FAO Clearinghouse initiative

  3. barque or container Why AOS vessel vessel ? boat ? craft ? ship ?

  4. Agricultural Ontology Service (AOS) Federated storage and description facility Consequently There is a need of a semantic approach Knowledge Organization Systems (KOSs)

  5. AOS Elements Ontology Registry Sub-domainontologies AGROVOCConcept Server Metadata ontologies • Domain concepts • Categories

  6. AGROVOC Concept Server

  7. AGROVOC Concept Server • Implementation phases • Phase 1: AGROVOC improvement • Cleaning phase • Conversion it into a more semantic representation • Export AGROVOC in several more standardized and formalised format (SKOS and OWL) • Integration of elements from the FAO glossary and the FAOTERM terminology system • Phase 2: Integrating and mapping additional terminologies (CGIAR, CAT)

  8. CS #1: AGROVOC cleaning and refinement AGROVOC OWL Current AGROVOC Improved AGROVOC Annotation Tool AGROVOC SKOS Using 3 Techniques: 1)Pattern matching 2)WordNet alignment 3) Rules 3.1) Expert Defined Rules 3.2) Semi-automatically Learned Rules

  9. CS #1: Annotation Tool (2)

  10. The rules-as-you-go approach (1) Exploit patterns to automate the conversion process1.   An editor has determined that milk NT cow milk should become milk <includesSpecific> cow milk • She recognizes that this is an example of the general pattern milk NT * milk  milk <includesSpecific> * milk (where * is the wildcard character) • Given this pattern, the system can derive automatically milk NT goat milk should become milk <includesSpecific> goat milk and milk <includesSpecific> buffalo milk

  11. The rules-as-you-go approach (2) • Pattern:Substance NT/RT Substance Substance <containsSubstance> Substance • PatternAnimal RT BodyPart Animal <hasComponent> BodyPart • PatternTaxon RT FoodProduct Taxon <usedToMake> FoodProduct

  12. CS: Phase #1 AGROVOC OWL Maintenance Area Improved AGROVOC MySQL AGROVOC SKOS

  13. CS: Phase #2 Maintenance Area Other Thesauri AGROVOC OWL AGROVOC MySQL integrate if needed AGROVOC SKOS

  14. CS enrichment • Multilinguality • Japanese (new), Hungarian, Korean, Lao, Marati • Agricultural terms: 4000 • Thesauri: • IWMI • CIP • CIAT • Chinese Agricultural Thesaurus • Definitions

  15. Note Relationships between Relationships Relationships between concepts Concept Relationship annotation relationship designated by Relationships between terms Lexicalization/ Term Other information: language/culture subvocabulary/scope audience type, etc. manifested as Relationships between strings String

  16. AOS issues • Relationships (concept level, term level, string level) • URI • Versioning • Quality control • Class / Instances • Performance • Reasoners

  17. AGROVOC OWL: sample

  18. Instances

  19. Metadata Schemes and Ontologies

  20. <dc:title /> <dc:creator /> < /> < /> < /> rights gmo dc:title dc:subject dc:author ---- --- ----- Document --- ---- -- --- ------- Metadata Schemas as Ontologies • The ontology layer offers: • the possibility of reasoning within the domain through precise specifications of concepts, relations, and rules • the possibility of inferring new data from existing data Sub-domain (subject) ontology Resource + authority files Metadata schema / AP Metadata Ontology

  21. A bibliographical metadata ontology FAOBIB DOCREP Merge + Transform • The ontology is composed of: • concepts • relationships between concepts • instances

  22. Ontology Portal for the Food, Nutrition and Agriculture Journal

  23. The FAO Clearinghouse initiative

  24. The FAO Clearinghouse initiative • Clearinghouse for Information Management Standards in Agriculture: • to facilitate collaboration, partnership and networking among partners by promoting information exchange and knowledge sharing. • to amalgamate the decentralized efforts currently taking place on development of methodologies, standards and applications for management of agricultural information systems; consequently, providing a ‘one-stop’ access to system designers and implementers.

  25. The FAO Clearinghouse initiative: goals • to establish a network of partners who adhere and agree to the primary goal of the initiative “to provide unified and free access to information management approaches and tools”, • to bring together information about currently available standards (such as thesauri, classification schemes, metadata sets, ontologies, controlled vocabularies) used in management of Agricultural Information, • to encourage the re-use of these standards to facilitate interoperability between information systems, • to increase awareness of these freely available resources, • to promote sharing of good practice examples, and • to provide channels for communication between different actors in the community.

  26. KOS registry Subjectontologies Metadata and AP ontologies kos <dc:title /> /> <ags:citation < /> < /> < /> Overall picture Clearinghouse Registry of Metadata Elements Concept Server • Subject concepts • Categories

  27. The FAO Clearinghouse web site

  28. Johannes Keizer Fynvola LehunteWard Anita LiangGauri SalokheMargherita Sinihttp://www.fao.org/agris/aos/ Sixth Agricultural Ontology Service Workshop on"Ontologies: the more practical issues and experiences“July 25-28, 2005, Vila Real, PortugalThank you.

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