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Smart organization of agricultural knowledge: the example of
the AGROVOC Concept Server and Agropedia
ISKO Italy Open conference systems, Paradigms and conceptual systems in KO
Roma, 24 February 2010
Few words about myself
Outline
• Why such projects• The AGROVOC Concept Server
– Benefits– Technology
• The Agropedia Project– Benefits– Technology
• Conclusion
Why such projects?
• Adding semantics to Agricultural Knowledge– Agricultural Ontology Service
• Scope– Better define and describe knowledge – Give meaning and structure to information– Enable reuse of domain knowledge– Avoid ambiguities– Allow better searches– Provide smart services– …
The starting idea…
• Semantic technologies were evolving– Ontologies– Concepts– URIs– Machine readable formats
• Everything started from AGROVOC…– Multi-lingual– Multi-domains– Re-engineering
Foundational Layer
Application Specific Layer
Domain Specific Layer
LexicalizationsFoundational Agricultural
Ontology
RiceOntology Pest
Ontology PlantOntology
AgriculturalDomainSpecificOntology
imports
IndianRice
Ontology
RiceCultivationOntology
impo
rts
ApplicationSpecificOntology
imports
Architecture of AOS ontologies
AGROVOC Concept Server
AGROVOC Concept Server
• A knowledge base of Agricultural related concepts organized in ontological relationships (hierarchical, associative, equivalence)
• Will contain 600.000 terms in around 20 languages
• Concepts can be organized in multiple categories
TerminologyWorkbench
AGROVOCOWL
AOS Core: the Concept Server
Export
AGROVOCRDFS formats
(e.g. SKOS)and
TagTextISO2709
Other thesauriand
terminologies
integration
ABACA NT1 Food NT2 AppleANIMAL BT Organ NT ....
mapping
Other thesauri & terminologies
ABACA NT1 Food NT2 AppleANIMAL BT Organ NT ....
Three levels of representation
• Concepts (the abstract meaning) – Ex: ‘rice’ in the sense of a plant,
• Terms (language-specific lexical forms) – Ex: ‘Rice’, ‘Riz’, ‘Arroz’, ‘ ’稻米 , or ‘Paddy’
• Term variants (the range of forms that can occur for each term)– Ex: ‘O. sativa’ or ‘Oryza Sativa’, ‘Organization’ or
Organisation’
Concept example
Organization
– hasLexicalization • Organizações (pt)• Organization (en) [P. T]
hasSpellingVariant» Organisation (uk-en)
– hasSubClass department (en)
– hasStatus• Published
– hasDateCreated• 12/12/2006
– hasDateUpdated• 01/10/2009
Semantic RelationshipsConcept to Concept
isA (hierarchy), isPestOf, hasPest
Concept to Term
hasLexicalization (links concepts to their lexical realizations)
Term to Term isSynonymOf, isTranslationOf, hasAcronym, hasAbbreviation
Term to String hasSpellingVariant, hasSingular
Towards the Concept Server
• AGROVOC cleaning and refinement
CurrentAGROVOC
MySQL
ImprovedAGROVOC
MySQL
AGROVOC OWLRevision
andRefinement
Ontology models (AGROVOC Concept Server, LIR, ...)
Concept
Relationshipsbetweenconcepts
Lexicalization/Term
String
Relationshipsbetweenstrings
Relationshipsbetweenterms
designated by
manifested asOther information:language/culture
subvocabulary/scopeaudiencetype, etc.
Note
annotation relationship
Relationship
RelationshipsbetweenRelationships
All terms are created as instances of the class o_terms. All at the same level. Only one language per term.
term level
string level
concept level
The Workbench
• A web-based working environment for managing the AGROVOC Concept Server
• Facilitate the collaborative editing of multilingual terminology and semantic concept information
• It includes administration and group management features
• It includes workflows for maintenance, validation and quality assurance of the data pool
Users/Roles/Groups
• Non registered users• Term editors• Ontology editors• Validators• Publishers• Administrators
Modules
• Home• Search• Concept/Term Management• Relationship Management• Classification Scheme Management• Validation• Consistency Check• Import/Export• User/Group Management• Statistics/Preferences
Concept/Term Management
Concept Relationship• Can create the concept-concept relationship• Inverse relationship is also created automatically
• Ex: If we create A affects B, then B isAffectedBy A relationship is also created
Graphical Visualization
Term Relationship
• Add/edit/delete term-term relationship
• Relationships can be– is scientific name of – has scientific name– has synonym– has translation– is acronym of – has acronym– has abbreviation
Term Spelling Variant
• Can assign the different spelling variant for the terms in different languages
• Ex: – color (us-en)– colour (uk-en)
Classification Schemes
RSS
Web services
System Architecture (1/2)
• Triple store database (MySQL and sesame)• System database (MySQL) • AJAX technology (Google Web Toolkit)• Java• Queries to the triple store using SEMRQL• Organized in modules
System Architecture (2/2)
Ontology repository (OWL)System Data Repository
Protégé OWL APIJDBC (MYSQL)
Validation
Stati
stics
Use
r M
anag
emen
t
Gro
up
Man
agem
ent
Syst
em
Pref
eren
ceGWT
Conc
ept
Man
agem
ent
Rela
tions
hip
Man
agem
ent
Sear
ch
Sche
me
Man
agem
ent
Impo
rt
Expo
rt
Cons
iste
ncy
Chec
k
AGROVOC WORKBENCH CONCEPT SERVER INTERFACE
Benefits• Agricultural related concepts will be uniquely identified
– URI-based indexing and search systems• Multiple terms in many languages (include spelling
variants, acronyms, dialectal forms or local terms used in specific geographical area)– freedom to use any language
• Ability of creating catalogues more machine-interpretable;
• More interoperability with other systems using ontologies – mapping and linking to other URI
Agropedia
What is Agropedia Indica
Knowledge Repository on Agriculture
Of universal knowledge models
And localized content
For a variety of users
With appropriate interfaces
Built in collaborative mode
In multiple languages
Rice
Saket-4
EnglishHindi
TeleguSpanish
this is a documentabout rice and its
pests.....
Once the rice ap-pear
in the world .....
Mad Cow Disea-se is the commonly
used name for Bovine
Spongiform Encephalopathy
(BSE) ....
Scope• Build an infrastructure of agricultural knowledge
– Multilingual and localized information – Knowledge Models (KMs) as conceptual reference– Different crops (Chickpea, Groundnut, Litchi, Pigeon pea, Rice, Sorghum, Sugarcane, Vegetable pea, and Wheat)– Domain specific information (local fertilizers, soil, cropping techniques and methods, …)
• Present it in various ways• Different stakeholders: scientists, students, extension workers,
farmers, policy makers, agronomists, soil scientists, plant breeders or geneticists, farm managers, and other experts
• Specific guidelines• Registry of relationships (object properties and data type
properties)
Knowledge Objects
this is a documentabout rice and its
pests.....Once the rice ap-
pearin the world .....
Mad Cow Disea-se is the commonly
used name for Bovine
Spongiform Encephalopathy
(BSE) ....
docs, pdf, txt, ... jpg, gif, bmp, ...wav, audio, ... htm, html, asp, php, ...
author: ...subject: ....identifier: ....
author: ...subject: ....identifier: ....
author: ...subject: ....identifier: ....
author: ...subject: ....identifier: ....
METADATA
URI
Retrieval
this is a documentabout rice and its
pests.....
Once the rice ap-pear
in the world .....
Mad Cow Disea-se is the commonly
used name for Bovine
Spongiform Encephalopathy
(BSE) ....
results.....
• Navigate knowledge maps• Concept indexing • Blogs (experts can create blog on specifics topics and
farmers can post questions and comments)• Q/A forum• FAQ• Agrowiki (a common platform where everyone can
share experiences)• Multilingual services
Services
Knowledge base structure
• Agricultural Experts can upload content as:– crops calendar– publications (journals, articles, magazines, thesis,
books)– do’s and don'ts (extension knowledge)– sponsor content – ...
• Content (except agrowiki) will be verified by experts• Agricultural related issues in Agrowiki
Conceptual Architecture
Digital Objects
Resource Layer
Semantic Layer
Interface LayerUser requests
Knowledge model serverUpload view Content
Technology• First release implemented using Alfresco• Subsequently, because of the need of incorporating
other functionalities, Drupal– blogs, chats, forums, Q/A, user management, etc.
• Cmap for the KMs, and exported in SVG format • Other formats (pdf, jpg) for visualization only• Java to customize the OWL version of the Kms• Taxonomy module for tagging and searching the
content• A Java module for automatic tagging using an the
KMs is in process of implementation.
Agropedia IndicaApplication UI
Technical infrastructure
MsqlUser
management
User requests
Upload view Content
Knowledge Models
• A knowledge model is a function of its use• For the same domain one needs multiple
models depending on the use/user• Researchers needed to identify these
different models and build them• Consistent and coherent
KM in Agropedia and AOS
Agropedia KMs
AGROVOC
30% 70%
16% of all concepts in Agropedia KM are scientific names or common names
16%
ENGLISH
Multilinguality
Generic model
Specific models
HINDITELUGU
....
Generic model
(Specific models from IITK)
translateAGROVOC Concept Server (via WS)
Innovative aspects
• Agropedia presents to users different semantically oriented tools: textual and audio blogs, wikis, forums, and the KMs presented in different formats (pdf, static or context-sensitive images)
• Users have the possibility to choose a preferred way of navigating the KMs
• Resources from the library catalogue are tagged with concepts from the KM
• No matter what languages the maps are displayed, the results will be always the same (currently, KMs exists in English and Hindi)
Agropedia
What are you interestedtoday?
- FAQ- Pesticides- Rice- Seasonal info- Agroclimatic zones-....
Who are you?
Agro-scientistExtension workerCall Center Operator
News
has Users
Sponsors
NAIPICAR
Partners
IITKIITBICRISATFAOGB PANT.....
has Sponsors
has Partners
FAQ Kisan Blog
has Services
has content...............
Home About
Knowledge Models in Agropedia
• Crop• Pesticides• Rice
– Rice pests– Rice diseases
• ... many others
Crop
Rice cropping system
Rice pests
Rice diseases (detail)
Insecticides (detail)
Relationships concept-to-conceptand instance-to-instance
Benefits
• Agropedia attempt to inject social networking and semantic technologies into Indian agriculture
• The Library section of the Agropedia is the expert certified knowledge
• Wiki, blogs, Forum provide the platform for un-regulated people-created content/knowledge
• Agropedia permits users to comment upon certified knowledge
To conclude…
Conclusion and Future Works• FAO and AOS partners invest in processable information• Agropedia opens the road to concept based maps in India• A lot still remains to do, in AGROVOC CS
• OWL2 + knowledge extraction
• In Agropedia• more KM + OWL for better services, e.g. Problem - solving
– what should I do if my rice is infested by gundhi bug?– where I can find seeds of good quality?– what should I do if rice new leaves start yellowing?
• Mutual integration to investigate (same users, …)• Linked Data (linkeddata.org) exposure
Application Specific Layer
Domain Specific Layer
Agropedia IndicaWorkbench
AGROVOC CSWorkbench
Future AOS Ontologies Interactions
IITK Modules
....ricemangosorghum
....organismssubstances
AGROVOC CS Modules
IndianRiceOntology@IITK
RiceOntology@IITK
sam
e U
RI
EcosystemsOntology@FAO
May translate upper level
models
AgriculturalDomain Specific
Ontologies
OtherSpecific
Ontologies
internet
Take home messages
• Semantic technologies can play a role for the agricultural domain
• Many stakeholders are involved(users / providers / developers)
• Agrovoc Concept Server and Agropedia are two project in this line
References
• http://aims.fao.org/• http://code.google.com/p/agrovoc-cs-
workbench/• http://agropedia.iitk.ac.in/
Thanks
Margherita Sini, Sachit Rajbhandari, Johannsen Gudrun, Jeetendra Singh, Johannes Keizer, Dagobert Soergel,T.V. Prabhakar, Asanee Kawtrakul