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Salvador Sánchez-Alonso, Jesús Cáceres Information Engineering research unit, University of Alcala, Spain Aage S. Holm, Geir Lieblein, Tor Arvid Breland.

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Presentation on theme: "Salvador Sánchez-Alonso, Jesús Cáceres Information Engineering research unit, University of Alcala, Spain Aage S. Holm, Geir Lieblein, Tor Arvid Breland."— Presentation transcript:

1 Salvador Sánchez-Alonso, Jesús Cáceres Information Engineering research unit, University of Alcala, Spain Aage S. Holm, Geir Lieblein, Tor Arvid Breland 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 Engineering an ontology on organic agriculture and agroecology: the case of the Organic.Edunet project Co-funded by the European Commission eContentplus programme

2 2/28 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

3 3/28 Openly-available portal Aimed at:  European youth  Producers  Farmers  Consumers

4 4/28 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

5 5/28 Search and Browse learning resources Find scenarios - using organic resources in teaching and learning Public Underpinned by.. Repository interface – manual or electronic upload Federated repository Database of scenarios Ontologies – to support search and retrieval Educational metadata (IEE LOM)

6 6/28 overall architecture

7 7/28 semantic services module

8 8/28 Procedure for the engineering of an ontology on OA & AE

9 9/28 overall coverage  15 partners from 10 EU countries

10 10/28 Negotiation  Partners with different  Backgrounds  Knowledge  Institutions  Language  must work together to deliver ontology in time as basis for rest of project

11 11/28 Engineering  No mature technologies  OA & AE experts elaborate a list  built upon previous efforts:  (http://www.bioagro.gr)  FAO's AGROVOC (http://www.fao.org/aims/ag_intro.htm)  and others  Includes mapping to those vocabularies

12 12/28 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:  Farming  Distribution  Processing  Consumption  Waste management

13 13/28 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

14 14/28 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

15 15/28 Evaluate using upper ontology  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.Find one or several terms in the OpenCyc upper ontology that subsume, are equal, or similar to the category under consideration. 2.Check carefully that the mapping is consistent with the rest of the subsumers inside the upper ontology. 3.Provide the appropriate predicates to characterize the new category. 4.Edit the term in an ontology editor to come up with the final formal version.

16 16/28 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

17 17/28 Procedure for the engineering of an ontology on OA & AE

18 18/28 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

19 19/28 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

20 20/28 Thesaurus sub-terms

21 21/28 Ontological relationships

22 22/28 Dimensions  Perspective  User  Space-and-time  Essential for advanced searches  To be included in content metadata

23 23/28 The perspective dimension  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

24 24/28 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

25 25/28 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 (plant- available N during a growing season), medium-term (N balance during a crop rotation) or long-term (humus N dynamics over decades) time horizons

26 26/28 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

27 27/28 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 ontology will be used in pilot searches, results helping refine design and content

28 28/28 connect to  need more information?  want to get affiliated with Organic.Edunet?  visit    


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