Building Ontologies for Algal Biomass Operations 2012
1. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
Building Ontologies for
Algal Biomass Operations
Monika Solanki
Knowledge Based Engineering Lab
Birmingham City University, UK
June 13, 2012
3. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
Outline
1 Motivation
2 Minimum Descriptive Language (MDL)
4. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
Outline
1 Motivation
2 Minimum Descriptive Language (MDL)
3 Ontology Development for Algal Biomass Production
5. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
Outline
1 Motivation
2 Minimum Descriptive Language (MDL)
3 Ontology Development for Algal Biomass Production
4 Working Demo
6. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
Algae as a source of food
Microalgae as a food source for humans has been
considered for overpopulated countries and for space
travel since as early as 1961.
If algae is grown under proper environmental conditions,
the protein yield from it may be quite high.
Algae have been collected for more than 4000 years in
China and Japan for use as human food.
Spirulina algae is considered to be one of the most
nutritious food on the planet.
8. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
Algaculture
Algal production operations can be quite diverse in the size
of the plant and the scope of their produce.
They vary from small units producing specialty chemicals
and nutraceuticals to large scale farms involved in the
production of food products and biofuels.
This diversity makes a uniform analysis of algal
productivity a challenging endeavour.
9. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
EnAlgae: Energetic Algae
Aims to reduce CO2 emissions and dependency on
unsustainable energy sources in North West Europe.
4 Year Strategic initiative of Interreg IVb NWE programme.
19 partners and 14 Observers across 7 EU states.
Coordinated set of activities focussing on sharing best
practice, developing effective stakeholder engagement and
encouraging transnational cooperation.
http://www.enalgae.eu/
10. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
EnAlgae: Some of the objectives
Accelerate development of sustainable technologies for
Biomass production.
Create a network of pilot scale algal facilities across NWE
in order to address the current lack of verifiable information
on algal productivity.
Maintain an up to date inventory in which pilots collect and
share data in a standardised manner.
Combine information across the entire algal bioenergy
delivery chain into a comprehensive and user friendly
Decision Support System for practitioners, policy makers
and investors
http://www.enalgae.eu/
11. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
The problem
Lack of a unified underlying standard that provides a set of
metrics to facilitate a uniform and accurate assessment of
the economic and environmental footprint of the
operations.
Lack of a shared, accumulative and consistent knowledge
base that can support funding bodies and investment
stakeholders in making decisions.
13. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
The Potential for Ontologies across the Algal
supply chain
14. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
Minimum Descriptive Language(MDL)
Standard developed by the Algal Biomass Organisation(ABO),
To uniformly capture the footprint of an algal production
operation.
To eliminate the prevailing heterogeneity in the recording of
plant-specific metrics
To facilitate the generation and sharing of a uniform and
consistent knowledge base
To harmonise the terminology to be used across
production operations and stakeholders.
http://www.algalbiomass.org/
15. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
Minimum Descriptive Language (MDL)
16. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
OntoMDL
Advantages of building ontologies from standards
Already built-in-consensus on the use of key domain
specific terminologies
Minimal semantic loss as standards informally include the
relationships between concepts and ease of knowledge
transfer.
20. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
Ontology Lifecycle: Phases
Guided by the Neon project,
Inception
Knowledge Acquisition
Assessment
Design
Implementation
http://www.neon-project.org/
21. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
Knowledge Acquisition Phase
An algal production unit can be
a newly established plant with no access to knowledge
bases from existing plants (Competitive markets can drive
the situation).
a newly established plant which has access to and would
like to benefit from knowledge bases acquired from
existing plants.
an existing plant which would like to benefit from a well
recorded history of knowledge bases.
22. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
Assessment Phase
Assessment of identified standards, assessing other
ontologies identified for reuse.
Merging ontologies, reengineering ontologies.
Refrain from using NLP techniques in the initial iterations.
A detailed perusal of the standards by knowledge
engineers, guided by domain experts, for knowledge
extraction.
Iterative evolution of the standards based on the ontologies
developed.
After a few iterations of the standards-ontology mapping, NLP
techniques guided by the lessons learned can be explored.
23. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
OntoMDL Conceptualisation
Core Concepts
ProcessInput
ProcessOutput
24. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
OntoMDL Conceptualisation
Specialisation
Process Input
CarbonInput
EnergyInput
WaterInput
25. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
OntoMDL Conceptualisation
Specialisation
Process Output
ConstituentProduct
IndirectProduct
LiquidWaste
26. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012
OntoMDL: Additional Conceptualisation
Background Knowledge
AlgalOperationUnit
AlgalOperationProcess
