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