1.
Webinars@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

3.
Introduction to KOS

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

5.
Focusing on ontologies

Ontology: Explicit formal specification
of terms in a domain AND the relations
among them

6.
Tree-view of an ontology

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


8.
Open Source KOS Management
tools (indicative list)

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.


11.
VocBench screenshot

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

13.
Protégé screenshot

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

15.
TemaTres screenshot

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

17.
Neologism screenshot

18.
The MoKi tool

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


20.
MoKi screenshot (2011)

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

22.
The Organic.Edunet ontology

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


24.
The Organic.Edunet ontology

Currently consists of 381 concepts
translated in 18 languages

25.
Translating the OE ontology
(2010)

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 subdomains 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

28.
Building the Organic.Edunet
ontology (3/3)

29.
Evolution of the Organic.Edunet
ontology using MoKi

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

34.
Discussions in MoKi

35.
Concept management

Refers to
 Editing concept
 Renaming concept
 Deleting concept

36.
Introduction of new concepts

Ontology suggestion service




Verified Keywords,
User (modified) Keywords,
(Automatically) Extracted Keywords and
Search-Query-Logs

37.
Translation of concepts
(2013)

38.
Mapping to AGROVOC

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

40.
Case study: the use of the
ontology service API

41.
OE ontology evolution in
numbers

42.
Next steps

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


46.
www.organic-lingua.eu
Contact me at:
vprot@agroknow.gr
Thank you!