Presentation made in the context of the FAO AIMS Webinar titled “Knowledge Organization Systems (KOS): Management of Classification Systems in the case of Organic.Edunet” (http://aims.fao.org/community/blogs/new-webinaraims-knowledge-organization-systems-kos-management-classification-systems)
21/2/2014
Barangay Council for the Protection of Children (BCPC) Orientation.pptx
KOS Management - The case of the Organic.Edunet Ontology
1. Webinar@AIMS, 21/2/2014
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
2. Contents of the presentation
(Short)
introduction to KOS
Open source KOS management tools
The MoKi tool
The Organic.Edunet ontology
Using MoKi for managing the
Organic.Edunet ontology
Next steps
4. About KOS
KOS = Knowledge Organization Systems
◦ a generic term used in knowledge
organization including the following types
◦ Term lists
Authority Files
Glossaries
Dictionaries
• Relationship
Lists
• Thesauri
• Semantic
Networks
• Topic maps
•
◦ Classifications & Categories Ontologies
Subject Headings
7. 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
9. Talking about KOS
management
Manage entries
◦ Add, revise, delete
Translate entries
Change relationships
Import existing lists of terms/concepts
Export the lists as OWL/SKOS
10. Tools: VocBench
available 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.
12. Tools: Protégé
Available at http://protege.stanford.edu
developed 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
14. Tools: TemaTres
Available at
http://www.vocabularyserver.com
a 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
16. Tools: Neologism
Available at: http://neologism.deri.ie/
developed by DERI (Digital Enterprise
Research Institute), Ireland
a vocabulary publishing platform for the
Web of Data
focuses on ease of use and compatibility
with Linked Data principles
◦ facilitates the creation of RDF classes and
properties
supports the RDFS standard, and a part of
OWL
Is NOT ontology/SKOS editor and does not
support multilingual labels
19. MoKi:
the Enterprise Modelling WiKi
Available at
https://moki.fbk.eu/website/index.php
Developed by FBK, Italy
Supports 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
21. MoKi evolution
During 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
23. The Organic.Edunet ontology
a 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
26. Building the Organic.Edunet
ontology (1/3)
OA & AE domain experts
elaborated a list including all the
relevant terms in the domain of OA &
AE
Using the list of terms as
input, domain experts identified
sub-domains with the aim of dividing
the original list into microthesauri
1.
2.
◦
with the help of librarians and guidance
from the ontology experts
27. Building the Organic.Edunet
ontology (2/3)
3.
4.
5.
Domain experts added agreed,
unambiguous definitions for the
terms, thus producing a “concept list”
Ontology experts developed an
initial ontology from the concept list
The ontology produced in the
previous step was evaluated making
use of upper ontologies
30. Time for evolution
Organic.Lingua ICT-PSP project (20112014)
◦ Aims to enhance the multilinguality options
of the Organic.Edunet Web portal
◦ provided the opportunity for updating &
revising the Organic.Edunet ontology
31. The requirements
Multilinguality
◦ Facilitate the translation processes
Avoid using spreadsheets for translations
Use of machine translation tools
◦ Automate process
Collaborative work
◦ Use web-based tool
◦ Enable discussions for concept revisions
◦ Enable different translations to take place at the
same time
Exposure
◦ Automatic exposure of the ontology through
API
32. 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
33. 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
36. Introduction of new concepts
Ontology suggestion service
Verified Keywords,
User (modified) Keywords,
(Automatically) Extracted Keywords and
Search-Query-Logs
39. Exposure of concepts
Ontology service = use of API
http://wiki.organiclingua.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
43. 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
44. 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
45. Acknowledgements
The 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