Transcript Slide 1

Co-funded by the European Commission
eContentplus programme
Engineering an ontology on organic
agriculture and agroecology: the case
of the Organic.Edunet project
Salvador Sánchez-Alonso, Jesús Cáceres
Information Engineering research unit, University of Alcala, Spain
Aage S. Holm, Geir Lieblein, Tor Arvid Breland
Norwegian University of Life Sciences
Nikos Manouselis
Informatics Laboratory, Agricultural University of Athens
Presented by
Roger Mills
Oxford University Library Services
Organic Agriculture and Agroecology
(OA & AE)
 Education is essential to raise public awareness of
OA/AE, in support of food security
 Organic.Edunet is
 an EU co-funded project
 3 years from Oct 2007
 €4.8m
 to provide
 an online, freely-available portal
 where learning materials on OA & AE can be published
 and accessed through specialized technologies to aid search,
retrieval and use of the collected content
http://www.organic-edunet.eu
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Openly-available portal
Aimed at:
 European youth
 Producers
 Farmers
 Consumers
http://www.organic-edunet.eu
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Stages in building
 develop metadata scheme for the
description of digital learning resources
 use to describe existing contents
 organise in repositories
 federate local repositories
 provide a common point of access
including advanced search capabilities,
based on the Semantic Web vision
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Public
Search and
Browse learning
resources
Find scenarios using organic
resources in
teaching and
learning
http://www.organic-edunet.eu
Underpinned by..
Repository
interface –
manual or
electronic upload
Database of
scenarios
Ontologies – to
support search and
retrieval
Federated
repository
Educational
metadata
(IEE LOM)
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Users
Web Portal Module
Organic.Edunet
Federation of Learning
Repositories
Semantic Services Module
BIO@GRO
ENOAT
ECOLOGICA/
COMPASS
Learning Repository Management Module
Content
Providers
Intute
Public
Resources
School
Learning Resources
Exchange Module
overall
architecture
Federation of Learning
Resource Repositories
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Organic.Edunet Web Portal Module
Semantic Services Module
Web Service Query
Interface
uses
Ontologies
WSMO LOMR
LOMR Module
RDF2WSMO
Converter
LOM2WSMO
Translator
Module
Metadata
Harversting
Mechanism
Learning Repository Management Module
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semantic
services
module
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Procedure for the
engineering of an
ontology on OA & AE
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overall coverage
 15
partners
from 10
EU
countries
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Negotiation
 Partners with different
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Backgrounds
Knowledge
Institutions
Language
 must work together to deliver ontology
in time as basis for rest of project
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Engineering
 No mature technologies
 OA & AE experts elaborate a list
 built upon previous efforts:
 Bio@gro (http://www.bioagro.gr)
 FAO's AGROVOC
(http://www.fao.org/aims/ag_intro.htm)
 and others
 Includes mapping to those vocabularies
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Formulate modules
 Domain [subject] experts identify sub-domains
 assisted by librarians and ontology experts
 dividing the original list into microthesauri or
modules
 Modules must be cohesive: all the concepts logically
related will be part of the same module
 Tentative high level modules:
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Farming
Distribution
Processing
Consumption
Waste management
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Add definitions
 Domain experts add agreed,
unambiguous definitions for the terms
 thus producing a “concept list”
 concept denotes terms whose definition
and relation to other concepts has been
established
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Develop initial ontology
 Ontology experts develop initial
ontology from concept list
 Process to engineer each module
separately into a sub-ontology
 All sub-ontologies created in parallel
from the definitions
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Evaluate using upper ontology
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OE uses the OpenCyc ontology: ‘hundreds of thousands of
terms and millions of assertions relating the terms to each
other, forming an upper ontology whose domain is “all of
human consensus reality”.’
Evaluation process structured around four steps:
1.
2.
3.
4.
Find one or several terms in the OpenCyc upper ontology that
subsume, are equal, or similar to the category under
consideration.
Check carefully that the mapping is consistent with the rest of
the subsumers inside the upper ontology.
Provide the appropriate predicates to characterize the new
category.
Edit the term in an ontology editor to come up with the final
formal version.
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Validation by example
 using scenarios of typical user interaction for
searching and retrieving resources
 to improve and refine the ontology
 an iterative and incremental process
 through which validated concepts and
relations will be added to the ontology in a
continuous and systematic building method
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Procedure for the
engineering of an
ontology on OA & AE
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Problems
 Domain experts preferred to see “the whole
picture” not just “their” module
 Solution to build up a full list of tagged
terms, where tags link terms to tentative
modules where they could fit
 Division of this “full map” of concepts into
cohesive modules in the next iteration
 Domain experts thus constructed a mind map
of concepts, where they could later easily
work with one “branch” at a time
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From lists of terms to ontologies
 ‘is a’ relationships need further
definition
 Subject experts needed to create those
definitions
 Ontology experts formalise relationship
 Once fully defined, ontology replaces
concept lists, thesauri and term lists
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Thesaurus sub-terms
Plant Production
Plant Health
Diversity
Preventative Plant
Protection
Crop Rotation
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Plant Production
Is an agricultural activity
Diversity
Plant Health
is a prerequisite for Plant Production
is a prerequisite for Plant Production in
organic agriculture
Plant protection
is a set of techniques and processes to
achieve Plant Health
Preventative Plant Protection
is a sub-set of techniques for Plant
Protection
Ontological
relationships
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Crop Rotation
is a specific technique for Preventive Plant Protection
is a specific technique for obtaining Diversity
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Dimensions
 Perspective
 User
 Space-and-time
 Essential for advanced searches
 To be included in content metadata
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The perspective dimension
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Social
Economic
Production
Environmental
 Different users focus on different aspects of same
subject
 E.g. “Plant protection”:
 Environmental: biological aspects of plant health & plant
protection, content of nutrients
 Economic: cost-benefit of different plant protection
strategies
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The user dimension
 Educational level
 Consumer
 Farmer/producer
 Different users view terms and relationships to other
terms with different levels of complexity
 E.g “labelling”:
 Consumer: ideology, structure of regulations and
accreditation
 Farmer/producer: specific regulations concerning plant
production
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The space-and-time dimension
 Spatial levels
 Time horizons
 Different users require information in relation to
different spatial and time characteristics
 E.g.:
 How wide: Symbiotic N2 fixation: at molecular, soil–plant
mesocosm, field, farm, local, national or biosphere spatial
levels
 How long: Soil nitrogen dynamics: within-year (plantavailable N during a growing season), medium-term (N
balance during a crop rotation) or long-term (humus N
dynamics over decades) time horizons
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Term selection
 Goal of OA & AE: “To produce enough and
healthy food for the world’s population in a
sustainable and ecological way”
 Initial term selection focus on keywords:
Health, Care, Natural, Ecology, Quality,
Safety, Variation, Diversity and Fair
 Terms chosen to relate to content
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Conclusion
 Collaborative process involving domain experts,
ontology experts, librarians, external consultants, etc
 So only the two first steps of the full procedure
completed:
February 2008: Preliminary list of terms
May 2008: Initial list of tagged concepts
September 2008: first version of the ontology (English only)
end of September: translations to all the other eight
languages represented in the Organic.Edunet consortium
 October 2008- ontology will be used in pilot searches, results
helping refine design and content
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connect to
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need more information?
want to get affiliated with
Organic.Edunet?
visit
 http://www.organic-edunet.eu
 email
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
[email protected]
[email protected]
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