Building Ontologies for Algal Biomass Operations 2012

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Building Ontologies for Algal Biomass Operations 2012

  1. 1. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012 Building Ontologies forAlgal Biomass Operations Monika SolankiKnowledge Based Engineering Lab Birmingham City University, UK June 13, 2012
  2. 2. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012Outline1 Motivation
  3. 3. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012Outline1 Motivation2 Minimum Descriptive Language (MDL)
  4. 4. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012Outline1 Motivation2 Minimum Descriptive Language (MDL)3 Ontology Development for Algal Biomass Production
  5. 5. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012Outline1 Motivation2 Minimum Descriptive Language (MDL)3 Ontology Development for Algal Biomass Production4 Working Demo
  6. 6. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012Algae 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.
  7. 7. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012Algaculture
  8. 8. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012Algaculture 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. 9. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012EnAlgae: 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. 10. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012EnAlgae: 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. 11. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012The 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.
  12. 12. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012Algal Supply Chain
  13. 13. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012The Potential for Ontologies across the Algalsupply chain
  14. 14. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012Minimum 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. 15. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012Minimum Descriptive Language (MDL)
  16. 16. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012OntoMDLAdvantages 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.
  17. 17. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012Scope of OntoMDL
  18. 18. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012Ontology Development Methodology
  19. 19. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012Ontology Lifecycle
  20. 20. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012Ontology Lifecycle: PhasesGuided by the Neon project, Inception Knowledge Acquisition Assessment Design Implementation http://www.neon-project.org/
  21. 21. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012Knowledge Acquisition PhaseAn 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. 22. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012Assessment 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, NLPtechniques guided by the lessons learned can be explored.
  23. 23. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012OntoMDL ConceptualisationCore Concepts ProcessInput ProcessOutput
  24. 24. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012OntoMDL ConceptualisationSpecialisation Process Input CarbonInput EnergyInput WaterInput
  25. 25. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012OntoMDL ConceptualisationSpecialisation Process Output ConstituentProduct IndirectProduct LiquidWaste
  26. 26. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012OntoMDL: Additional ConceptualisationBackground Knowledge AlgalOperationUnit AlgalOperationProcess
  27. 27. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012OntoMDL
  28. 28. monika.solanki@bcu.ac.uk Semantic Web and Agri-Food, 13th June 2012 Working Demo

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