Semantic Technologies at FAO International Society for Knowledge Organization (ISKO) 3 Aprile 2009, Torino Margherita Sini

Few words about myself

Just a very rapid introduction What? semantic, semantic web, semantic technologies ontologies, Knowledge Organization Systems, metadata Why? interoperability, exchange, share user orientation, precision and recall multilinguality, cultural views, context Who? everybody, all domains, all countries, all .org Which instruments? experts, NLP, methodologies and techniques

What?

semantic, semantic web, semantic technologies

ontologies, Knowledge Organization Systems,

metadata

Why?

interoperability, exchange, share

user orientation, precision and recall

multilinguality, cultural views, context

Who?

everybody, all domains, all countries, all .org

Which instruments?

experts, NLP, methodologies and techniques

Outline Semantic projects involving FAO AOS IPFSAPH, FNA, CWR, Fisheries, Food & nutrition, Geopolitical ontology, AGROVOC Concept Server Thai Rice Onto, Agropedia Indica Methods and Methodologies Ontology models (AGROVOC Concept Server, LIR, ...) Methods What’s next registries: concepts, relationships guidelines networked ontologies ontology-based applications collaborations

Semantic projects involving FAO

AOS

IPFSAPH, FNA, CWR, Fisheries, Food & nutrition, Geopolitical ontology, AGROVOC Concept Server

Thai Rice Onto, Agropedia Indica

Methods and Methodologies

Ontology models (AGROVOC Concept Server, LIR, ...)

Methods

What’s next

registries: concepts, relationships

guidelines

networked ontologies

ontology-based applications

collaborations

Semantic projects involving FAO

Why AOS vessel? craft? boat? bateaux? barco? Terminology brokering Semantic navigation, Clustering, Ranking, ... Intelligent query expansion Interoperability ship or container Inferencing Reasoning Machine learning

Agricultural Ontology Service An FAO initiative for more coherence in Agricultural Information Systems Need of a semantic approach AOS elements: AGROVOC Concept Server KOS registry Mapping registries Metadata standards Tools Publications (guidelines, ...) Built from AGROVOC Domain concepts Categories AGROVOC Concept Server Ontology registry Sub-domain ontologies Metadata ontologies

An FAO initiative for more coherence in Agricultural Information Systems

Need of a semantic approach

AOS elements:

AGROVOC Concept Server

KOS registry

Mapping registries

Metadata standards

Tools

Publications (guidelines, ...)

Built from AGROVOC

Domain concepts

Categories

IPFSAPH

IPFSAPH

The Ontology

Creation of the core ontology 1600 concepts Information Resources Brainstorming Codex Alimentarius SPS Agreement Ontology Ontology Editor (OI-Modeler) Agrovoc Food Safety Documents Generic Documents subject specialists

Information Resources

Brainstorming

Codex Alimentarius

SPS Agreement

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

Multilinguality The system is also able to understand a concept even when different languages are used.

Check spelling Spelling errors are corrected: e.g. “desease” into “disease”

Paraphrasing “ mad cow disease symptoms” or “clinical signs of bovine spongiform encephalopathy”

give the same results, which are ranked.

Semantic navigation of the bibliographical metadata (1)

Semantic navigation of the bibliographical metadata (2)

Semantic Navigation of Knowledge parent concept(s) children concept(s)

FNA

Creation of the core ontology BIBLIOGRAPHIC DATABASE CORPORATE DOCUMENT REPOSITORY DATABASE MERGE RECORDS + TRANSFORM TO RDFS Ontology Editor (OI-Modeler) maintain

Ontology Relationships

The ontology concepts Publication Issue Work Article Subject Term Category Author Region Language Year

Publication

Issue

Work

Article

Subject Term

Category

Author

Region

Language

Year

The ontology instances

Features Multilingual concept resolution Get suggestions for the navigation (e.g. synonyms) Guided query formulation Easy navigation of the objects by following the semantic links

Multilingual concept resolution

Get suggestions for the navigation (e.g. synonyms)

Guided query formulation

Easy navigation of the objects by following the semantic links

What is possible to do Improve browsing: e.g. continent / regions / countries e.g. link Agris/Caris categories with keywords concept identification through natural language processing: spell checking, parsing ( e.g. “Dietary guidelines for human nutrition” or “Anaemia in children ”) Perform some inferencing: 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.) get the co-authors show articles with the same set or related keywords

Improve browsing:

e.g. continent / regions / countries

e.g. link Agris/Caris categories with keywords

concept identification through natural language processing: spell checking, parsing ( e.g. “Dietary guidelines for human nutrition” or “Anaemia in children ”)

Perform some inferencing:

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.)

get the co-authors

show articles with the same set or related keywords

RDFa

CWR

Hierarchy

Undertaken by FAO Developed in harmony with CWR descriptor list First version (English only) available by December 2006 About 800 core terms + acronyms + spelling variants; Clearly definition of concepts (AGROVOC + other sources); and Relationships: hierarchical + causative The project

Undertaken by FAO

Developed in harmony with CWR descriptor list

First version (English only) available by December 2006

About 800 core terms + acronyms + spelling variants;

Clearly definition of concepts (AGROVOC + other sources); and

Relationships: hierarchical + causative

Knowledge sharing and reuse is now the primary goal of research communities worldwide. 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: sharing a common understanding of the structure of the information provided; formalizing and reusing the domain knowledge; analyzing it; and separating it from operational knowledge.

Knowledge sharing and reuse is now the primary goal of research communities worldwide.

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:

sharing a common understanding of the structure of the information provided;

formalizing and reusing the domain knowledge;

analyzing it; and

separating it from operational knowledge.

More semantics Term: wild plants subclass of plants superclass of crop wild relatives adapted by domestication benefits from resource conservation

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.”

CWR Crop wild relatives ontology (CWR ontology) The Ontology contains about 400 terms grouped into themes (different namespaces used) OWL Full

Crop wild relatives ontology (CWR ontology)

The Ontology contains about 400 terms

grouped into themes (different namespaces used)

OWL Full

Properties (1/2)

Properties (2/2)

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

Overall Solution (2/2) Data

Fisheries

The initial goal Making information interchangeable between ASFA, FIGIS, OneFish and AGROVOC The approach Creating an ontology, integrating or mapping the 3 different systems + AGROVOC Linking of the Ontology through wrappers to the different Information Systems Evolution: NeOn Fisheries Ontologies

The initial goal

Making information interchangeable between ASFA, FIGIS, OneFish and AGROVOC

The approach

Creating an ontology, integrating or mapping the 3 different systems + AGROVOC

Linking of the Ontology through wrappers to the different Information Systems

Evolution: NeOn

Foundational Ontology FOS core FOS integrated FOS merged FIGIS Reference Tables ASFA FIGIS DTD ONE FISH AGROVOC

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

Features Form versus meaning: Traditional Search Concept Search Implemented functionalities: synonym search multilingual capability terminology brokering disambiguation related concepts query expansion Basic natural language queries Semantic navigation of bibliographical metadata Semantic Navigation of Knowledge Alphabetic list ... Core Fishery Concepts ...

Form versus meaning:

Traditional Search

Concept Search

Implemented functionalities:

synonym search

multilingual capability

terminology brokering

disambiguation

related concepts

query expansion

Basic natural language queries

Semantic navigation of bibliographical metadata

Semantic Navigation of Knowledge

Alphabetic list ...

Core Fishery Concepts ...

Ontology properties

Example "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"

"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"

Using multilingual lexicalizations ENGLISH SPANISH FRENCH

Using hierarchically related concepts hierarchically related concept Polyvalent Trawlers

Using non-hierarchically related concepts non-hierarchically related concept gears

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:

Enhancement of terminologies

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”

Semantic Navigation of Knowledge: a) Thesaurus based Highlighting the originator thesaurus. User can select a specific thesaurus to look for.

Geopolitical ontology

Geopolitical ontology Incorporate geopolitical data Will serve as a bridge to allow communication between the various systems.

Incorporate geopolitical data

Will serve as a bridge to allow communication between the various systems.

Properties isValidFrom hasOfficialName hasCode isSuccessorOf hasBorderWith dependsOn

isValidFrom

hasOfficialName

hasCode

isSuccessorOf

hasBorderWith

dependsOn

Nutrition Ontology

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;http://www.fao.org/aos/infoods#&quot; xmlns:protege=&quot;http://protege.stanford.edu/plugins/owl/protege#&quot; xmlns:rdf=&quot;http://www.w3.org/1999/02/22-rdf-syntax-ns#&quot; xmlns:xsd=&quot;http://www.w3.org/2001/XMLSchema#&quot; xmlns:rdfs=&quot;http://www.w3.org/2000/01/rdf-schema#&quot; xmlns:owl=&quot;http://www.w3.org/2002/07/owl#&quot; xmlns:daml=&quot;http://www.daml.org/2001/03/daml+oil#&quot; xmlns:dc=&quot;http://purl.org/dc/elements/1.1/&quot; xml:base=&quot;http://www.fao.org/aos/infoods&quot;> <owl:Ontology rdf:about=&quot;&quot;> <owl:imports rdf:resource=&quot;http://protege.stanford.edu/plugins/owl/protege&quot;/> <owl:versionInfo rdf:datatype=&quot;http://www.w3.org/2001/XMLSchema#string&quot; >Revision 4.0</owl:versionInfo> <protege:defaultLanguage rdf:datatype=&quot;http://www.w3.org/2001/XMLSchema#string&quot; >en</protege:defaultLanguage> <rdfs:comment rdf:datatype=&quot;http://www.w3.org/2001/XMLSchema#string&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;http://www.w3.org/2001/XMLSchema#string&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>

AGROVOC

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 ....

Concept Server project Refine semantics and enrich data pool and lexicon Develop a workbench for terminology and ontology development and maintenance. Support information management specialists in the development, maintenance, and quality assurance of the AOS/CS Global knowledge vs local knowledge

Refine semantics and enrich data pool and lexicon

Develop a workbench for terminology and ontology development and maintenance.

Support information management specialists in the development, maintenance, and quality assurance of the AOS/CS

Global knowledge vs local knowledge

AGROVOC Concept Server AGROVOC cleaning and refinement Current AGROVOC MySQL Improved AGROVOC MySQL AGROVOC OWL Revision and Refinement

AGROVOC cleaning and refinement

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

Modelling Conversion to UTF-8 Migration to MySQL (from SQL server) Migration to PostgreSQL (from MySQL) Incorporated AGRIS/CARIS classification scheme (multilingual) and the mapping with AGROVOC keywords Modified structure to store multiple classification schemes Revised RDBMS scheme for ontology representation Designed OWL models Export to OWL format (v0.8a) Export to SKOS format (v0.8a)

Conversion to UTF-8

Migration to MySQL (from SQL server)

Migration to PostgreSQL (from MySQL)

Incorporated AGRIS/CARIS classification scheme (multilingual) and the mapping with AGROVOC keywords

Modified structure to store multiple classification schemes

Revised RDBMS scheme for ontology representation

Designed OWL models

Export to OWL format (v0.8a)

Export to SKOS format (v0.8a)

Methods Concepts from descriptors Synonym <owl:DatatypeProperty rdf:ID=&quot;synonym&quot;> Acronyms <owl:AnnotationProperty rdf:about=&quot;http://www.fao.org/aos/agrovoc/2005#acronym&quot;> < owl:Class rdf:about=&quot; http://www.fao.org/aos/agrovoc/2005 #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; http://www.fao.org/aos/agrovoc/2005 #c_3397&quot;/> <rdfs:subClassOf rdf:resource=&quot; http://www.fao.org/aos/agrovoc/2005 #c_32543&quot;/> </owl:Class>

Concepts from descriptors

Synonym <owl:DatatypeProperty rdf:ID=&quot;synonym&quot;>

Acronyms <owl:AnnotationProperty rdf:about=&quot;http://www.fao.org/aos/agrovoc/2005#acronym&quot;>

SKOS SKOS export from AGROVOC Concept Server Workbench (WB) SKOS web services SKOS-services for DSpace plug-in SKOS for mapping projects

SKOS export from AGROVOC Concept Server Workbench (WB)

SKOS web services

SKOS-services for DSpace plug-in

SKOS for mapping projects

SKOS export maintain access access response AGROVOC CS Workbench triple store Web Services

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

Agropedia Indica

References http://www.slideshare.net/marghe_rita/1-pantnagar http://www.slideshare.net/marghe_rita/2-pantnagar-w-guidelines http://www.slideshare.net/marghe_rita/3-pantnagar-w-exercices http://agropedia.iitk.ac.in/

http://www.slideshare.net/marghe_rita/1-pantnagar

http://www.slideshare.net/marghe_rita/2-pantnagar-w-guidelines

http://www.slideshare.net/marghe_rita/3-pantnagar-w-exercices

http://agropedia.iitk.ac.in/

Thai Rice Ontology

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

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

Conclusions

Ontology-based applications Networked Ontologies Better exploitation of the potentiality at the application level: powerful IR No more words but URIs in IS Ontology Web services (OWS)

Networked Ontologies

Better exploitation of the potentiality at the application level: powerful IR

No more words but URIs in IS

Ontology Web services (OWS)

Collaborations With AOS partners Within EU Projects NeOn SEMIC.EU With other initiatives GFIS Ecoterm Mapping projects GBIF Global Biodiversity Information Facility secretariat JRC + BGS + Biblioteca Nazionale di Firenze

With AOS partners

Within EU Projects

NeOn

SEMIC.EU

With other initiatives

GFIS

Ecoterm

Mapping projects

GBIF Global Biodiversity Information Facility secretariat

JRC + BGS + Biblioteca Nazionale di Firenze

Take-home message There are many uses for terminology & ontology systems in food and agriculture, both for information access and information processing FAO has several projects using such systems FAO is deploying the Agricultural Ontology Server (AOS) as a global resource SKOS and other knowledge representation standards play a key role

There are many uses for terminology & ontology systems in food and agriculture, both for information access and information processing

FAO has several projects using such systems

FAO is deploying the Agricultural Ontology Server (AOS) as a global resource

SKOS and other knowledge representation standards play a key role

Questions? Thanks Margherita Sini: margherita.Sini@fao.org Johannes Keizer: Johannes.Keizer@fao.org Dagobert Soergel: dsoergel@umd.edu 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...