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Knowledge Organization Systems
(KOS):
Management of Classification Systems
in the case of Organic.Edunet”
Vassilis Protonotarios,
Agricultural Biotechnologist, PhD
Agro-Know, Greece / University of Alcalá, Spain
Webinars@AIMS
21/2/2014
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Contents of the presentation
(Short) introduction to KOSOpen source KOS management
toolsThe MoKi toolThe Organic.Edunet ontologyUsing MoKi for managing the
Organic.Edunet ontologyNext steps
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Introduction to KOS
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About KOS
KOS = Knowledge Organization Systems◦a generic term used in
knowledge organization including the following types
◦Term lists Authority Files Glossaries Dictionaries
◦Classifications & Categories Subject Headings
• Relationship Lists• Thesauri• Semantic
Networks• Topic maps• Ontologies
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Focusing on ontologiesOntology: Explicit formal
specification of terms in a domain AND the relations among them
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Tree-view of an ontology
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But why use KOSs?A standardized mean for referring
to the same concept using a unique name
A mean for the classification of different resources in a domain
…and of course the backbone of linking heterogeneous data sources
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Open Source KOS Management tools (indicative list)
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Talking about KOS managementManage entries
◦Add, revise, deleteTranslate entriesChange relationshipsImport existing lists of
terms/conceptsExport the lists as OWL/SKOS
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Tools: VocBenchavailable at
http://vocbench.uniroma2.it/ developed by FAO and its partners;a web-based, multilingual, editing
and workflow tool;manages thesauri, authority lists and
glossaries using SKOS;facilitates the collaborative editing of
multilingual terminology and semantic concept information.
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VocBench screenshot
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Tools: ProtégéAvailable at
http://protege.stanford.edudeveloped by the Stanford Center for
Biomedical Informatics Research at the Stanford University School of Medicine;
ontology editor and knowledge-base framework;
supports modeling ontologies via a web client or a desktop client;
Protégé ontologies can be developed in a variety of formats including OWL, RDF(S), and XML Schema
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Protégé screenshot
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Tools: TemaTresAvailable at
http://www.vocabularyserver.coma tool for the development &
management of controlled vocabularies, thesauri, taxonomies other types of formal representations of
knowledge
ensures consistency & integrity of data and relationships between terms
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TemaTres screenshot
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Tools: NeologismAvailable at: http://neologism.deri.ie/ developed by DERI (Digital Enterprise
Research Institute), Irelanda vocabulary publishing platform for the
Web of Datafocuses on ease of use and compatibility
with Linked Data principles◦ facilitates the creation of RDF classes and
propertiessupports the RDFS standard, and a part of
OWLIs NOT ontology/SKOS editor and does not
support multilingual labels
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Neologism screenshot
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The MoKi tool
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MoKi: the Enterprise Modelling
WiKiAvailable at https://moki.fbk.eu/website/index.php
Developed by FBK, ItalySupports the construction of
integrated domain & process models
Easy editing of a wiki page by means of forms
Automatic import and export in OWL and BPMN
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MoKi screenshot (2011)
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MoKi evolutionDuring the Organic.Lingua ICT/PSP project:
◦Multilinguality options Integration of three machine translation services
◦Ontology enrichment services Automatically suggests new concepts for the
ontology
◦Mapping component Used for mapping the OE ontology to AGROVOC
◦Collaboration options Decisions made on discussions
◦Ontology service Exposure of ontology through REST API
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The Organic.Edunet ontology
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The Organic.Edunet ontologya conceptual model useful for
classifying learning materials on the Organic Agriculture (OA) and Agroecology (AE) domain
Developed in the context of the Organic.Edunet eContentPlus project
Used by Organic.Edunet for the classification of educational resources
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The Organic.Edunet ontologyCurrently consists of 381
concepts translated in 18 languages
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Translating the OE ontology (2010)
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Building the Organic.Edunet ontology (1/3)
1. OA & AE domain experts elaborated a list including all the relevant terms in the domain of OA & AE
2. Using the list of terms as input, domain experts identified sub-domains with the aim of dividing the original list into microthesauri
◦ with the help of librarians and guidance from the ontology experts
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Building the Organic.Edunet ontology (2/3)
3. Domain experts added agreed, unambiguous definitions for the terms, thus producing a “concept list”
4. Ontology experts developed an initial ontology from the concept list
5. The ontology produced in the previous step was evaluated making use of upper ontologies
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Building the Organic.Edunet ontology (3/3)
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Evolution of the Organic.Edunet ontology using MoKi
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Time for evolutionOrganic.Lingua ICT-PSP project
(2011-2014)◦Aims to enhance the multilinguality
options of the Organic.Edunet Web portal
◦provided the opportunity for updating & revising the Organic.Edunet ontology
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The requirementsMultilinguality
◦Facilitate the translation processes Avoid using spreadsheets for translations Use of machine translation tools
◦Automate processCollaborative work
◦Use web-based tool◦Enable discussions for concept revisions◦Enable different translations to take place at
the same timeExposure
◦Automatic exposure of the ontology through API
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The process (1/2)Formation of teams
◦Ontology experts / knowledge engineers◦Domain experts◦Language experts
Definition of tasks◦Deprecation of less-frequently used
concepts◦Refinement of most widely-used concepts◦Addition of new concepts◦Translation of concepts◦Mapping concepts to AGROVOC
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The process (2/2)Development of scenarios
◦A number of scenarios was developed per task & with specific deadlines
Collaborative work◦Discussions in MoKi◦Evolution based on discussions◦Validation of revisions by experts
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Discussions in MoKi
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Concept managementRefers to
Editing concept Renaming concept Deleting concept
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Introduction of new conceptsOntology suggestion service
Verified Keywords, User (modified) Keywords, (Automatically) Extracted Keywords and Search-Query-Logs
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Translation of concepts (2013)
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Mapping to AGROVOC
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Exposure of conceptsOntology service = use of APIhttp://wiki.organic-lingua.eu/APIs#Ontology_Service_API
◦Publish/expose the ontology◦Enable up-to-date publishing
Two different interfaces:◦Linked Open Data (LOD): Provides
data in SKOS format◦RESTful RDF: Exposes data in OWL2
or LOD format
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Case study: the use of the ontology service API
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OE ontology evolution in numbers
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Next steps
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Next steps in the ontology evolution (1/2)
Further work on the concepts◦Introduction of new concepts◦Refinement of existing ones◦Deprecation of existing ones◦Translation of concepts in additional
languages◦Mapping of the ontology to
additional ones
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Next steps in the ontology evolution (2/2)
Publication of ontology as linked data◦Definition of a namespace◦Ensure compliance with existing
standards
Link ontology with other related ones◦Already linked to AGROVOC
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AcknowledgementsThe Organic.Edunet ontology was
developed in the context of the Organic.Edunet project under the eContentPlus Programme
Parts of the work described in this presentation were partially funded by the Organic.Lingua project under the ICT Policy Support Programme