A presentation of developments in agriOpenLink project

1. Adaptive Agricultural Processes
via Open Interfaces and Linked
Services
IKT der Zukunft (1. Ausschreibung 2012)
Budget: ~800 k Euro
Laufzeit: 06/2013 - 05/2016 (36 Mo)
Anwendungsfeld: Produktionssysteme
Themenfelder:
• Datendurchdringen
• Semantische Technologien
• Schnittstellen von Systemen
Dr. Dana Tomic
FTW Forschungszentrum Telekommunikation
Wien, Austria

2. Advanced Technology
• ICT, Sensors, robots, GPS, Decision
Support Systems, Reporting, Tracking,
Tracing
• Showcase for the Internet of (or with)
Things
• Plug-and-play
Benefits
• Cost savings, quality improvement
• High precision of application, impact
reduction, sustainability
• Process optimization
From Data to Knowledge
• Data integration
• Knowledge management
• Add-value services

6. API
+
tool
box

8. Information and Advisory System
Workflow
Management
Semantic
Service
Composition
Semantic
Services
Process
Model for
Optimization
Ontologies
(Domain,
Services,
Sensors,
Interfaces)
Interface Data Models
Hardware Platforms

9. 





10. - Vocabularies = concepts and relationships (also referred to as “terms”)
used to describe and represent an area of concern.
- classify the terms
- characterize possible relationships
- define possible constraints on using those terms.
- can be very complex (with several thousands of terms) or very simple
(describing one or two concepts only).
- Ontology = explicit formal specifications of the terms in the domain and
relations among them
- Classes, Object Properties, Data Properties, Instances
- Reasoning = Classification, creation of new facts
- Description techniques:
- RDF and RDF Schemas
- Simple Knowledge Organization System (SKOS)
- Web Ontology Language (OWL)
- Rule Interchange Format (RIF).

11.  FAO - Food and Agriculture Organization of the United Nations (FAO;
http://aims.fao.org) - is developing agriculture information
management standards such as AGROVOC thesaurus, Agris and
openAgris.
 AGROVOC:
- a controlled vocabulary covering all areas of interest to FAO, including
food, nutrition, agriculture, fisheries, forestry, environment etc.
- formalized as a RDF/SKOS-XL linked dataset
- accessible through a SPARQL endpoint
- Available as open linked data, used for labeling of AGRIS data
 Other thesauri and ontologies ( USDA, CSRO, MUNI ontology)

12.  To share common understanding of the structure of information
among people or software agents
 To enable reuse of domain knowledge
 To make domain assumptions explicit
 To separate domain knowledge from the operational knowledge
 To analyze domain knowledge

13.  To share common understanding of the structure of information
among people or software agents
 To enable reuse of domain knowledge
 To make domain assumptions explicit
 To separate domain knowledge from the operational knowledge
 To analyze domain knowledge

28.  To share common understanding of the structure of information
among people or software agents
 To enable reuse of domain knowledge
 To analyze domain knowledge
 To make domain assumptions explicit
 To separate domain knowledge from the operational knowledge
 To have benefit of automatic reasoning

29. Maintain domain
knowledge
Collect Data in
Repository
Data
Ontology
Rules
Trigger Reasoner
Trigger Actions
based on Results of
Reasoning
Actions

30.  Determine the domain and scope of the ontology
- Use Cases: 1) Diary Farming 2) Precision Farming
- System Ontology
- Service Ontology
 Consider reusing existing ontologies
- Agriculture domain, upper ontologies, sensor ontologies
 Enumerate important terms in the ontology
- Farm, Animal, Milk, Food, Equipment, Users, Services, Process, …
 Identify relationships
 Translate into classes & properties
- Specify primitive classes
- Specify defined classes (for classification based on reasoning)
 Define individuals

32. Work Structure, Timeline, Main Results
Use Cases
Plugins
(Plugin
Services)
Ontologies
Platform & System
Services
Test
Application
User Study
Developer Tools
Use-Case Services

34. Plugins for agricultural
equipment
Core Decision Plugins/sWS
User Interaction Plugins/sWS

35. System Detailed Architecture
Ontology and Process Management System (OPMS)
(distributed: farm computer & remote platform(s) )
in detail
Web Server
Ontology &
Rule Mng.
Composition
Engine
Service
Quality Mng
Execution
Engine
Process
Quality Mng.
Asset Config.
Mng.
CD
W
S
UI
W
S
Publish and Subscribe
Web Interface
Web Server
Ontologies
Service & Process
Description Repository
Q&R (SPARQL &
policy-based reasoning)
Web Plugin
Server
Service &
Process
Registry
Core Plugin S.
UI Plugin S.
Eq. Plugin S.
Web Server
Q&R (SPARQL &
policy-based reasoning)
Data
Reposit.
Analytics

36.  Domain Modelling
- Detailed modelling of process in selected use cases
- The roles of stakeholders in the process: farmer, veterinarian, milk
company, quality assurance organization, animal tracing organization,
farmer associations
- Selection of ontologies, ontology development
- Extensibility by design
 Current Implementation
- Plug-in API development
- Sematic REST services (SADI approach)
- Service execution environment
 Next Steps
- Workflow modelling and matchmaking component
- Monitoring and service selection framework
- Recommendation framework

37. Dr. Slobodanka Dana Kathrin Tomic
Senior Researcher | FTW | www.ftw.at
Forschungszentrum Telekommunikation Wien GmbH
Donau-City-Straße 1/3 | A-1220 Vienna | Austria
+43/1/5052830 -54 | fax -99 | +43/6769129023