Semantic Technologies at FAO


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Several projects related to semantic technologies realized or ongoing at FAO

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Semantic Technologies at FAO

  1. 1. Semantic Technologies at FAO International Society for Knowledge Organization (ISKO) 3 Aprile 2009, Torino Margherita Sini
  2. 2. Few words about myself
  3. 3. Just a very rapid introduction <ul><li>What? </li></ul><ul><ul><li>semantic, semantic web, semantic technologies </li></ul></ul><ul><ul><li>ontologies, Knowledge Organization Systems, </li></ul></ul><ul><ul><li>metadata </li></ul></ul><ul><li>Why? </li></ul><ul><ul><li>interoperability, exchange, share </li></ul></ul><ul><ul><li>user orientation, precision and recall </li></ul></ul><ul><ul><li>multilinguality, cultural views, context </li></ul></ul><ul><li>Who? </li></ul><ul><ul><li>everybody, all domains, all countries, all .org </li></ul></ul><ul><li>Which instruments? </li></ul><ul><ul><li>experts, NLP, methodologies and techniques </li></ul></ul>
  4. 4. Outline <ul><li>Semantic projects involving FAO </li></ul><ul><ul><li>AOS </li></ul></ul><ul><ul><li>IPFSAPH, FNA, CWR, Fisheries, Food & nutrition, Geopolitical ontology, AGROVOC Concept Server </li></ul></ul><ul><ul><li>Thai Rice Onto, Agropedia Indica </li></ul></ul><ul><li>Methods and Methodologies </li></ul><ul><ul><li>Ontology models (AGROVOC Concept Server, LIR, ...) </li></ul></ul><ul><ul><li>Methods </li></ul></ul><ul><li>What’s next </li></ul><ul><ul><li>registries: concepts, relationships </li></ul></ul><ul><ul><li>guidelines </li></ul></ul><ul><ul><li>networked ontologies </li></ul></ul><ul><ul><li>ontology-based applications </li></ul></ul><ul><ul><li>collaborations </li></ul></ul>
  5. 5. Semantic projects involving FAO
  6. 6. Why AOS vessel? craft? boat? bateaux? barco? Terminology brokering Semantic navigation, Clustering, Ranking, ... Intelligent query expansion Interoperability ship or container Inferencing Reasoning Machine learning
  7. 7. Agricultural Ontology Service <ul><li>An FAO initiative for more coherence in Agricultural Information Systems </li></ul><ul><li>Need of a semantic approach </li></ul><ul><li>AOS elements: </li></ul><ul><ul><li>AGROVOC Concept Server </li></ul></ul><ul><ul><li>KOS registry </li></ul></ul><ul><ul><li>Mapping registries </li></ul></ul><ul><ul><li>Metadata standards </li></ul></ul><ul><ul><li>Tools </li></ul></ul><ul><ul><li>Publications (guidelines, ...) </li></ul></ul><ul><li>Built from AGROVOC </li></ul><ul><li>Domain concepts </li></ul><ul><li>Categories </li></ul>AGROVOC Concept Server Ontology registry Sub-domain ontologies Metadata ontologies
  8. 8. IPFSAPH
  9. 9. IPFSAPH
  10. 10. The Ontology
  11. 11. Creation of the core ontology 1600 concepts <ul><li>Information Resources </li></ul><ul><li>Brainstorming </li></ul><ul><li>Codex Alimentarius </li></ul><ul><li>SPS Agreement </li></ul>Ontology Ontology Editor (OI-Modeler) Agrovoc Food Safety Documents Generic Documents subject specialists
  12. 12. Concept Search The same records will be retrieved regardless of the specific synonyms or singular/plural forms that the user uses to refer to a concept. Related concepts
  13. 13. Multilinguality The system is also able to understand a concept even when different languages are used.
  14. 14. Check spelling Spelling errors are corrected: e.g. “desease” into “disease”
  15. 15. Paraphrasing “ mad cow disease symptoms” or “clinical signs of bovine spongiform encephalopathy”
  16. 16. give the same results, which are ranked.
  17. 17. Semantic navigation of the bibliographical metadata (1)
  18. 18. Semantic navigation of the bibliographical metadata (2)
  19. 19. Semantic Navigation of Knowledge parent concept(s) children concept(s)
  20. 20. FNA
  22. 23. Ontology Relationships
  23. 24. The ontology concepts <ul><li>Publication </li></ul><ul><li>Issue </li></ul><ul><li>Work </li></ul><ul><ul><li>Article </li></ul></ul><ul><li>Subject Term </li></ul><ul><li>Category </li></ul><ul><li>Author </li></ul><ul><li>Region </li></ul><ul><li>Language </li></ul><ul><li>Year </li></ul>
  24. 25. The ontology instances
  25. 26. Features <ul><li>Multilingual concept resolution </li></ul><ul><li>Get suggestions for the navigation (e.g. synonyms) </li></ul><ul><li>Guided query formulation </li></ul><ul><li>Easy navigation of the objects by following the semantic links </li></ul>
  26. 27. What is possible to do <ul><li>Improve browsing: </li></ul><ul><ul><li>e.g. continent / regions / countries </li></ul></ul><ul><ul><li>e.g. link Agris/Caris categories with keywords </li></ul></ul><ul><ul><li>concept identification through natural language processing: spell checking, parsing ( e.g. “Dietary guidelines for human nutrition” or “Anaemia in children ”) </li></ul></ul><ul><li>Perform some inferencing: </li></ul><ul><ul><li>get the authors associated with specific keywords or vice versa (“what an author wrote about between two years”, “who wrote about famine in 1999”, etc.) </li></ul></ul><ul><ul><li>get the co-authors </li></ul></ul><ul><ul><li>show articles with the same set or related keywords </li></ul></ul>
  27. 28. RDFa
  28. 29. CWR
  29. 30. Hierarchy
  30. 31. <ul><li>Undertaken by FAO </li></ul><ul><li>Developed in harmony with CWR descriptor list </li></ul><ul><li>First version (English only) available by December 2006 </li></ul><ul><li>About 800 core terms + acronyms + spelling variants; </li></ul><ul><li>Clearly definition of concepts (AGROVOC + other sources); and </li></ul><ul><li>Relationships: hierarchical + causative </li></ul>The project
  31. 32. <ul><li>Knowledge sharing and reuse is now the primary goal of research communities worldwide. </li></ul><ul><li>An ontology defines a common vocabulary; its potential is enormous. For researchers, scientists, extension workers, decision- and policy-makers, who need to share information about a specialized domain an ontology allows for: </li></ul><ul><li>sharing a common understanding of the structure of the information provided; </li></ul><ul><li>formalizing and reusing the domain knowledge; </li></ul><ul><li>analyzing it; and </li></ul><ul><li>separating it from operational knowledge. </li></ul>
  32. 33. More semantics Term: wild plants subclass of plants superclass of crop wild relatives adapted by domestication benefits from resource conservation
  33. 34. From unstructured data to formalized data “ Destruction of forests is leading to the loss of many populations of important wild relatives of fruit , nut and industrial crops such as mango and rubber.”
  34. 35. CWR <ul><li>Crop wild relatives ontology (CWR ontology) </li></ul><ul><li>The Ontology contains about 400 terms </li></ul><ul><ul><li>grouped into themes (different namespaces used) </li></ul></ul><ul><li>OWL Full </li></ul>
  35. 36. Properties (1/2)
  36. 37. Properties (2/2)
  37. 38. Overall Solution (1/2) Value-added information services Shared layer of interoperability Distributed Datasets dataset1 dataset2 Common exchange layer (Vocabularies,Ontologies, RDF/XML) … … datasetn Aggregated Database View Subject specific Portals Information System (n) News feed service
  38. 39. Overall Solution (2/2) Data
  39. 40. Fisheries
  40. 41. <ul><li>The initial goal </li></ul><ul><ul><li>Making information interchangeable between ASFA, FIGIS, OneFish and AGROVOC </li></ul></ul><ul><li>The approach </li></ul><ul><ul><li>Creating an ontology, integrating or mapping the 3 different systems + AGROVOC </li></ul></ul><ul><ul><li>Linking of the Ontology through wrappers to the different Information Systems </li></ul></ul><ul><li>Evolution: NeOn </li></ul>Fisheries Ontologies
  41. 42. Foundational Ontology FOS core FOS integrated FOS merged FIGIS Reference Tables ASFA FIGIS DTD ONE FISH AGROVOC
  42. 43. Fisheries Ontologies (2/2) OneFish FIGIS AGROVOC Aquaculture Resource Water Area land strains Species life cycle Farming system management system Production center Spawning technique Breeding technique Hatchery technique Expl. form Regulation Farming technique Environment Institution Health monitoring technique diseases suppliers ASFA
  43. 44. Features <ul><li>Form versus meaning: </li></ul><ul><ul><li>Traditional Search </li></ul></ul><ul><ul><li>Concept Search </li></ul></ul><ul><li>Implemented functionalities: </li></ul><ul><ul><li>synonym search </li></ul></ul><ul><ul><li>multilingual capability </li></ul></ul><ul><ul><li>terminology brokering </li></ul></ul><ul><ul><li>disambiguation </li></ul></ul><ul><ul><li>related concepts </li></ul></ul><ul><ul><li>query expansion </li></ul></ul><ul><li>Basic natural language queries </li></ul><ul><li>Semantic navigation of bibliographical metadata </li></ul><ul><li>Semantic Navigation of Knowledge </li></ul><ul><ul><li>Alphabetic list ... </li></ul></ul><ul><ul><li>Core Fishery Concepts ... </li></ul></ul>
  44. 45. Ontology properties
  45. 46. Example <ul><li>&quot;tell me what vessels from a nearby country are currently in the marine area 50N060W within Atlantic Ocean, provided that also some Thunnus alalunga stock can be fished by those vessels, through allowed techniques&quot; </li></ul>
  46. 47. Using multilingual lexicalizations ENGLISH SPANISH FRENCH
  47. 48. Using hierarchically related concepts hierarchically related concept Polyvalent Trawlers
  48. 49. Using non-hierarchically related concepts non-hierarchically related concept gears
  49. 50. Help the user formulate queries Original query: bateau de pêche To refine your query, click on the concepts you are interested in. They will appear to the left. Search:
  50. 51. Enhancement of terminologies
  51. 52. Reconcile different vocabularies “ navire de p ê che”, “fishing vessel”, “ embarcaciones de pesca” AGROVOC or ASFA or other “ fishing vessels,” “ fishing boat,” AGROVOC: “fishing vessels”, “barco”, etc... ASFA: “fishing vessels”
  52. 53. Semantic Navigation of Knowledge: a) Thesaurus based Highlighting the originator thesaurus. User can select a specific thesaurus to look for.
  53. 54. Geopolitical ontology
  54. 55. Geopolitical ontology <ul><li>Incorporate geopolitical data </li></ul><ul><li>Will serve as a bridge to allow communication between the various systems. </li></ul>
  55. 56. Properties <ul><li>isValidFrom </li></ul><ul><li>hasOfficialName </li></ul><ul><li>hasCode </li></ul><ul><li>isSuccessorOf </li></ul><ul><li>hasBorderWith </li></ul><ul><li>dependsOn </li></ul>
  56. 57. Nutrition Ontology
  57. 59. Procedure =CONCATENATE(&quot;<owl:Class rdf:ID=&quot;&quot;&quot;,J2,&quot;&quot;&quot;><rdfs:subClassOf><owl:Class rdf:ID=&quot;&quot;c_&quot;,B2,&quot;&quot;&quot;/></rdfs:subClassOf><rdfs:label xml:lang=&quot;&quot;en&quot;&quot;><![CDATA[&quot;,D2,&quot;]]></rdfs:label><code><![CDATA[&quot;,J2,&quot;]]></code><TAGNAME><![CDATA[&quot;,J2,&quot;]]></TAGNAME>&quot;,S2, T2,&quot;</owl:Class>&quot;) <?xml version=&quot;1.0&quot;?> <rdf:RDF xmlns=&quot;; xmlns:protege=&quot;; xmlns:rdf=&quot;; xmlns:xsd=&quot;; xmlns:rdfs=&quot;; xmlns:owl=&quot;; xmlns:daml=&quot;; xmlns:dc=&quot;; xml:base=&quot;;> <owl:Ontology rdf:about=&quot;&quot;> <owl:imports rdf:resource=&quot;;/> <owl:versionInfo rdf:datatype=&quot;; >Revision 4.0</owl:versionInfo> <protege:defaultLanguage rdf:datatype=&quot;; >en</protege:defaultLanguage> <rdfs:comment rdf:datatype=&quot;; >International Network of Food Data Systems (INFOODS) was established in 1984 on the basis of the recommendations of an international group convened under the auspices of the United Nations University (UNU). Its goal was to .....</rdfs:comment> </owl:Ontology> <owl:Class rdf:ID=&quot;c_0413&quot;> <code rdf:datatype=&quot;; >0413</code> <rdfs:subClassOf> <owl:Class rdf:ID=&quot;c_041&quot;/> </rdfs:subClassOf> <rdfs:label xml:lang=&quot;en&quot;>Vitamin D</rdfs:label> </owl:Class>
  58. 60. AGROVOC
  59. 61. AOS Core: the Concept Server Export mapping Terminology Workbench AGROVOC OWL AGROVOC RDFS formats (e.g. SKOS) and TagText ISO2709 Other thesauri and terminologies integration ABACA NT1 Food NT2 Apple ANIMAL BT Organ NT .... Other thesauri & terminologies ABACA NT1 Food NT2 Apple ANIMAL BT Organ NT ....
  60. 62. Concept Server project <ul><li>Refine semantics and enrich data pool and lexicon </li></ul><ul><li>Develop a workbench for terminology and ontology development and maintenance. </li></ul><ul><li>Support information management specialists in the development, maintenance, and quality assurance of the AOS/CS </li></ul><ul><li>Global knowledge vs local knowledge </li></ul>
  61. 63. AGROVOC Concept Server <ul><li>AGROVOC cleaning and refinement </li></ul>Current AGROVOC MySQL Improved AGROVOC MySQL AGROVOC OWL Revision and Refinement
  62. 64. How to obtain more semantics MAIZE UF corn NT flint maize NT popcorn NT sweet corn   MILK NT Milk Fat NT Colostrum NT Cow Milk International Fund for Agricultural Development UF IFAD MAIZE synonym corn superclass-of flint maize used-to-make popcorn hybridized-into sweet corn   MILK ingredient Milk Fat ingredient Colostrum superclass-of Cow Milk International Fund for Agricultural Development acronym IFAD
  63. 65. Modelling <ul><ul><li>Conversion to UTF-8 </li></ul></ul><ul><ul><li>Migration to MySQL (from SQL server) </li></ul></ul><ul><ul><li>Migration to PostgreSQL (from MySQL) </li></ul></ul><ul><ul><li>Incorporated AGRIS/CARIS classification scheme (multilingual) and the mapping with AGROVOC keywords </li></ul></ul><ul><ul><li>Modified structure to store multiple classification schemes </li></ul></ul><ul><ul><li>Revised RDBMS scheme for ontology representation </li></ul></ul><ul><ul><li>Designed OWL models </li></ul></ul><ul><ul><li>Export to OWL format (v0.8a) </li></ul></ul><ul><ul><li>Export to SKOS format (v0.8a) </li></ul></ul>
  64. 66. Methods <ul><li>Concepts from descriptors </li></ul><ul><li>Synonym <owl:DatatypeProperty rdf:ID=&quot;synonym&quot;> </li></ul><ul><li>Acronyms <owl:AnnotationProperty rdf:about=&quot;;> </li></ul>< owl:Class rdf:about=&quot; #c_3&quot;> <rdfs:label xml:lang=&quot;en&quot;>ABA</rdfs:label> <rdfs:label xml:lang=&quot;fr&quot;>ABA</rdfs:label> <rdfs:label xml:lang=&quot;es&quot;>ABA</rdfs:label> <rdfs:label xml:lang=&quot;ar&quot;>آبا</rdfs:label> <rdfs:label xml:lang=&quot;zh&quot;>脱落酸</rdfs:label> <synonym xml:lang=&quot;en&quot;>[8565] Abscisic acid</synonym> <rdfs:subClassOf rdf:resource=&quot; #c_3397&quot;/> <rdfs:subClassOf rdf:resource=&quot; #c_32543&quot;/> </owl:Class>
  65. 67. SKOS <ul><li>SKOS export from AGROVOC Concept Server Workbench (WB) </li></ul><ul><li>SKOS web services </li></ul><ul><ul><li>SKOS-services for DSpace plug-in </li></ul></ul><ul><li>SKOS for mapping projects </li></ul>
  66. 68. SKOS export maintain access access response AGROVOC CS Workbench triple store Web Services
  67. 69. Ontology models (AGROVOC Concept Server, LIR, ...) Concept Relationships between concepts Lexicalization/ Term String Relationships between strings Relationships between terms designated by manifested as Other information: language/culture subvocabulary/scope audience type, etc. Note annotation relationship Relationship Relationships between Relationships All terms are created as instances of the class o_terms. All at the same level. Only one language per term. term level string level concept level
  68. 70. Agropedia Indica
  69. 71. References <ul><li> </li></ul><ul><li> </li></ul><ul><li> </li></ul><ul><li> </li></ul>
  70. 72. Thai Rice Ontology
  71. 73. Plant ontology: Relationship types Taxon <hasSuperclass> Taxon Taxon <has GrowthType> GrowthType Taxon <hasPropagationMethod> PropagationMethod Taxon <occursIn> Environment Taxon <hasPest> Taxon Taxon <hasDisease> Disease Disease <causedBy> Taxon TaxonPart <isa> AnatomicalPart TaxonPart <isa> AnatomicalTypeOfFruit TaxonPart <partOf> Taxon TaxonPart <usedAs> Use TaxonPart <usedToMake> ProductType Taxon <hasDescription> Text
  72. 74. Thai plant ontology: Example Mangifera indica Linn. <hasSuperclass> Mangifera Mangifera indica Linn. <hasGrowthType> tree Mangifera indica Linn. <hasPropagationMethod> seedling Mangifera indica Linn <hasDescription> &quot;leaves ...., flower ...... “ Mangifera indica Linn <occursIn> dry soil Mangifera indica Linn. <hasPest> Scirtothrips dosalis Hood Mangifera indica Linn <hasPest> Oidium mangiferae OR, instead of the last statement or in addition to it Mangifera indica Linn <hasDisease> Powdery Mildew Powdery mildew <caused by> Oidium mangiferae
  73. 75. Conclusions
  74. 76. Ontology-based applications <ul><li>Networked Ontologies </li></ul><ul><li>Better exploitation of the potentiality at the application level: powerful IR </li></ul><ul><li>No more words but URIs in IS </li></ul><ul><li>Ontology Web services (OWS) </li></ul>
  75. 77. Collaborations <ul><li>With AOS partners </li></ul><ul><li>Within EU Projects </li></ul><ul><ul><li>NeOn </li></ul></ul><ul><ul><li>SEMIC.EU </li></ul></ul><ul><li>With other initiatives </li></ul><ul><ul><li>GFIS </li></ul></ul><ul><ul><li>Ecoterm </li></ul></ul><ul><li>Mapping projects </li></ul><ul><li>GBIF Global Biodiversity Information Facility secretariat </li></ul><ul><li>JRC + BGS + Biblioteca Nazionale di Firenze </li></ul>
  76. 78. Take-home message <ul><li>There are many uses for terminology & ontology systems in food and agriculture, both for information access and information processing </li></ul><ul><li>FAO has several projects using such systems </li></ul><ul><li>FAO is deploying the Agricultural Ontology Server (AOS) as a global resource </li></ul><ul><li>SKOS and other knowledge representation standards play a key role </li></ul>
  77. 79. Questions? Thanks Margherita Sini: Johannes Keizer: Dagobert Soergel: Asanee Kawtrakul: [email_address] But Also: Gudrun Johannsen, Boris Lauser, Claudio Baldassarre, Gauri Salokhe, Marta Iglesias, Caterina Caracciolo, Sachit Rajbhandari, Jeetendra Singh, Mary Redahan, Shrestha, Prashanta, Ton, Imm, Thanapth, Trakul, and many others...