Slide 1 The Agricultural Ontology Service (AOS) Effort for Content Standardization in Agriculture Frehiwot Fisseha (UNFAO)

Slide 2 Outline FAO’s mandate in agricultural information management Problems we want to solve The current situation Proposed solution The Agricultural Ontology Service (AOS) AOS prototype (The Fishery Ontology Service)

Slide 3 FAO’s mandate FAO’s main goal is to reduce the number of hungry people by 50% within the year WAICENT (World Agricultural Information Center) is FAO’s approach to fight hunger with information. FAO produces a huge amount of data/information in agriculture and related disciplines. It is also within FAO’s mandate to make available agriculture related information from other information providers. FAO collaborates in information networks which are dedicated to the dissemination of agricultural domain.

Slide 4 Problems we want to solve “The Information Organization” problem faced by Information Managers At present most information management tasks are performed manually.... consider the cataloging and indexing task…. Manual cataloging and indexing are labor-intensive processes, requiring special training. Tools for automating or semi-automating these processes are much in demand.

Slide 5 Problems we want to solve Both parameters are ranking low today! Recall: Number of Relevant Documents in the Collection Number of Relevant Documents Identified Precision: Number of Relevant Documents Identified Total Number of Documents Identified “The Information Retrieval” problem faced by Information Users

Slide 6 Problems we want to solve Topic Trees from categorization schemes and thesauri are rigid and not very expressive Machine produced clusters are “flexible”, but imprecise and at times out of context

Slide 7 Knowledge Organizations Systems: Metadata Schema The subject categorization schemes are not adequately developed to be of use for semantic description for web resources The metadata schemas are closely attached to traditional description of bibliographical records The Dublin Core Metadata Initiative (DCMI) is a step forward to define core metadata to describe information objects Effort is underway to develop Agricultural metadata standards

Slide 8 Knowledge Organization Systems: Vocabularies AGROVOC NAL Thesaurus CABI Thesaurus Dedicated KOSs Non-dedicated KOSs e.g., ASFA thesaurus e.g., the Multilingual Forestry Thesaurus e.g., the Sustainable Development website classification e.g., biological taxonomies such as NCBI and ITIS GEMET Other thematic thesauri Existing Thesauri and Knowledge Organization Systems (KOSs)  Common concepts are not declared  No or very limited interoperability  Insufficient subject + language coverage  Severe maintenance problems  Very limited machine readability  Only very simple encoding of semantic relations

Slide 9 Some observations No cross navigation between applications Full text search engines based on statistical text analysis are imprecise  Systems based only on “machine intelligence” do not show too promising results Web crawlers and harvesters do good jobs only on already structured information sources.  Recognition of meaning (semantic analysis) by machines is only possible by using using structured meta-information and formal knowledge description Agreed metadata schemas Controlled vocabularies, Taxonomies

Slide 10 The solution we propose- Domain Ontology  An ontology is a formal knowledge organization system A formal description of the application knowledge It contains concepts and their definitions Relations between concepts Possibility for machine processing

Slide 11 What benefits do we expect from Ontology? –Semantic Organization of websites Knowledge maps Guided discovery of knowledge Easy retrievability of information without using complicated Boolean logic –Text processing by machines Text Mining on the Web (meaning-oriented access) Automatic indexing and text annotation tools Full text search engines that create meaningful classification (FAO- Schwartz not related to FAO) (semantic clustering) –Intelligent search of the Web Building dynamical catalogues from machine readable meta data Cross Domain Search –Natural Language processing Better machine translation Queries using natural language

Slide 12 Guided Browse and Search Facilities Records found: 5 1. xxxxxxxxxxx 2. xxxxxxxxxxx 3. xxxxxxxxxxx 4. xxxxxxxxxxx 5. xxxxxxxxxxx Biotopes Cropping systems using forests Economics of forest production Forestry equipment Soil science You may also be interested in... What would you like to view? Forest rights issues Parasites of forests Pesticides used in forests Types of forest products Uses of forest products Geographic area You can further limit by: x Africa Web page Type of resource

Slide 13 Context Sensitive Knowledge Access Conservation agriculture Farmers like it because it gives them a means of conserving, improving and making more efficient use of their natural resources About camels and llamas Descendants of the same rabbit-sized mammal, they have become two of humanity's most versatile domestic animals Agribusiness and small farmers Well managed contract farming contributes to both increased income for producers and higher profits for investors Toward biosecurity Biological and environmental risks associated with food and agriculture have intensified with economic globalization Urban food marketing In the “century of cities”, a major challenge will be providing adequate quantities of nutritional and affordable food for urban inhabitants Crop science and ethics In order to continue their contribution to human development, crop scientists must regain credibility Use your right mouse button to learn more about an italicized word on the page. Biosecurity: management of all biological and environmental risks associated with food and agriculture, including forestry and fisheries See also: Biosafety Food Safety Risk Management Or are you interested in...: Food Security Biological Diversity Agricultural Web Page

Slide 14 The Collaborative Approach We Want to Adopt Only agreed semantic standards guarantee knowledge discovery between different applications. Developing Knowledge Organization Systems is resource intensive and requires stakeholder’s agreement and participation. Hence, FAO started initiatives to bring interested partners together The AGStandards initiative was launched in October, 2000 to agree on agricultural metadata standards The Agricultural Ontology Service (AOS) concept paper was publicized in July 2001.

Slide 15 What does Agricultural Ontology Service mean? The Agricultural Ontology Service is an approach to organize knowledge organization systems that is  International The Internet must become multilingual  Multidisciplinary The field of agriculture is broad and multidisciplinary.  Cooperative Stakeholders can contribute different expert knowledge  Distributed No central ownership  Coordinated Coordination must ensure reusability and standardization

Slide 16 AOS: Iterative Knowledge Registration KOS uses components to build an application Discussions and choices for amendments to components Components: terms, definitions, relationships Users search and browse application using components User feedback Agricultural Ontology Service (AOS) Federated storage and description facility Components: terms, definitions, relationships

Slide 17 Activities to date The first AOS workshop took place in Rome, November 2001 –A launch group was established with participation of –Content providers (FAO, CABI) –Solution providers in the Agricultural Area (ATO -Wageningen, University of Florida) –Ontology development Groups (AIFB Karlsruhe, CNR Italy) –Ontology experts  The second AOS workshop (January 2002 in Oxford) –Decision to develop prototypes as proof of concept. –The Fishery Ontology Service (FOS) is one of the prototypes  The third AOS workshop took (May 2002 Florida) –Decision to setup the AOS consortium

Slide 18 AOS – a “business model” A consortium of Information Providers A clearinghouse for semantic standards in agriculture and related discipline. One stop access to agreed standards (Ontologies, Metadata schemas, Vocabularies…). Participation as a consortium in semantic web activities (Ontoweb). Organization of seminars and workshops to further develop and promote the use of semantic standards.

Slide 19 AOS Prototype- The Fishery Ontology Service (FOS) Goal: to integrate the multilingual fishery and aquatic resources terminology –the oneFish Community Directory, –ASFA, – FIGIS, –AGROVOC Objective: –to have a better tool for document indexing and information retrieval, –to promote interaction and knowledge sharing within the fishery community