Transcript Smart organization of agricultural knowledge: the example

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
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–
–
–
–
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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
Architecture of AOS ontologies
imports
Lexicalizations
Rice
Ontology
Pest
Ontology
Indian
Rice
Ontology
Foundational Agricultural
Ontology
Plant
Ontology
Rice
Cultivation
Ontology
Agricultural
Domain
Specific
Ontology
Application
Specific
Ontology
Foundational Layer
Domain Specific Layer
Application Specific Layer
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
AOS Core: the Concept Server
Other thesauri
and
terminologies
ABACA
NT1 Food
NT2 Apple
ANIMAL
BT Organ
NT ....
integration
Other thesauri
& terminologies
ABACA
NT1 Food
NT2 Apple
ANIMAL
BT Organ
NT ....
AGROVOC
OWL
mapping
Export
Terminology
Workbench
AGROVOC
RDFS formats
(e.g. SKOS)
and
TagText
ISO2709
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 Relationships
Concept 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
Current
AGROVOC
MySQL
Revision
and
Refinement
Improved
AGROVOC
MySQL
AGROVOC
OWL
Ontology models (AGROVOC Concept Server, LIR, ...)
concept level
Relationships
between
Relationships
Relationships
between
concepts
Relationship
Concept
designated by
Relationships
between
terms
annotation
relationship
string level
Lexicalization/
term level
Term
All terms are created as instances of the class o_terms. All at the same
level. Only one language per term.
Other information:
Note
manifested as
language/culture
Relationships
between
subvocabulary/scope
strings
audience
String
type, etc.
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 termterm 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)
•
•
•
•
•
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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)
AGROVOC WORKBENCH CONCEPT SERVER INTERFACE
Relationship
Management
Concept
Management
Scheme
Management
Search
Export
Import
Consistency
Check
Statistics
System
Preference
Group
Management
User
Management
GWT
Validation
JDBC (MYSQL)
System Data Repository
Protégé OWL API
Ontology repository (OWL)
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 machineinterpretable;
• More interoperability with other systems using
ontologies
– mapping and linking to other URI
Agropedia
What is Agropedia Indica
Knowledge Repository on Agriculture
Rice
this is a document
Once
about
ricethe
andrice
its apMad Cow
Diseapear
pests.....
se is theincommonly
the world .....
used name
for Bovine
Spongiform
Encephalopathy
(BSE) ....
Of universal knowledge models
And localized content
Saket-4
For a variety of users
With appropriate interfaces
Built in collaborative mode
In multiple languages
Telegu
Spanish
Hindi
English
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
METADATA
author: ...
author: ...
author: ...
author: ...
subject: .... subject: .... subject: .... subject: ....
identifier: .... identifier: .... identifier: .... identifier: ....
URI
this is a document
about
rice the
andrice
its apOnce
pests.....
pear
Mad Cow
Diseain the world .....
se is the commonly
used name
for Bovine
Spongiform
Encephalopathy
(BSE) ....
docs, pdf, txt, ...
jpg, gif, bmp, ...
wav, audio, ...
htm, html, asp, php, ...
Retrieval
this is a document
Once
about
ricethe
andrice
its apMad Cow
Diseapear
pests.....
se is theincommonly
the world .....
used name
for Bovine
Spongiform
Encephalopathy
(BSE) ....
results.....
Services
• 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
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
User
requests
Upload view Content
Interface Layer
Semantic Layer
Resource Layer
Digital Objects
Knowledge
model server
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.
Technical infrastructure
User
management
Msql
Agropedia Indica
Application UI
Upload view Content
User
requests
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
30%
AGROVOC
70%
16%
16% of all concepts in Agropedia KM are scientific names
or common names
Multilinguality
Generic model
ENGLISH
Specific models
AGROVOC Concept Server (via WS)
Generic model
(Specific models from IITK)
translate
HINDI
TELUGU
....
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)
Home
About
News
FAQ
Kisan Blog
has Services
NAIP
has Sponsors ICAR
Who are you?
has Users
Agro-scientist
Extension worker
Call Center Operator
Partners
Agropedia
has Partners
What are you interested
today?
- FAQ
- Pesticides
- Rice
- Seasonal info
- Agroclimatic zones
-....
Sponsors
IITK
IITB
ICRISAT
FAO
GB PANT
.....
has content
.....
.....
.....
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-concept
and 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 unregulated 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
Future AOS Ontologies Interactions
AGROVOC CS Modules
AGROVOC CS
Workbench
....
organisms
substances
internet
Ecosystems
Ontology@FAO
May translate
upper level
models
Other
Specific
Ontologies
IITK Modules
....
rice
mango
sorghum
Domain Specific Layer
Agropedia Indica
Workbench
Agricultural
Domain Specific
Ontologies
Rice
Ontology@IITK
Indian
Rice
Ontology@IITK
Application Specific Layer
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-csworkbench/
• http://agropedia.iitk.ac.in/
Thanks
Margherita Sini, Sachit Rajbhandari,
Johannsen Gudrun, Jeetendra Singh,
Johannes Keizer, Dagobert Soergel,
T.V. Prabhakar, Asanee Kawtrakul