Building Ontologies for Algal Biomass Operations 2012

[email protected] 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


Outline

1 Motivation

2 Minimum Descriptive Language (MDL)

3 Ontology Development for Algal Biomass Production

4 Working Demo


Algae as a source of food

Microalgae as a food source for humans has beenconsidered for overpopulated countries and for spacetravel 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 inChina and Japan for use as human food.Spirulina algae is considered to be one of the mostnutritious food on the planet.


Algaculture


Algaculture

Algal production operations can be quite diverse in the sizeof the plant and the scope of their produce.They vary from small units producing specialty chemicalsand nutraceuticals to large scale farms involved in theproduction of food products and biofuels.This diversity makes a uniform analysis of algalproductivity a challenging endeavour.


EnAlgae: Energetic Algae

Aims to reduce CO2 emissions and dependency onunsustainable 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 bestpractice, developing effective stakeholder engagement andencouraging transnational cooperation.

http://www.enalgae.eu/



EnAlgae: Some of the objectives

Accelerate development of sustainable technologies forBiomass production.Create a network of pilot scale algal facilities across NWEin order to address the current lack of verifiable informationon algal productivity.Maintain an up to date inventory in which pilots collect andshare data in a standardised manner.Combine information across the entire algal bioenergydelivery chain into a comprehensive and user friendlyDecision Support System for practitioners, policy makersand investors




The problem

Lack of a unified underlying standard that provides a set ofmetrics to facilitate a uniform and accurate assessment ofthe economic and environmental footprint of theoperations.Lack of a shared, accumulative and consistent knowledgebase that can support funding bodies and investmentstakeholders in making decisions.


Algal Supply Chain


The Potential for Ontologies across the Algalsupply chain


Minimum Descriptive Language(MDL)

Standard developed by the Algal Biomass Organisation(ABO),To uniformly capture the footprint of an algal productionoperation.To eliminate the prevailing heterogeneity in the recording ofplant-specific metricsTo facilitate the generation and sharing of a uniform andconsistent knowledge baseTo harmonise the terminology to be used acrossproduction operations and stakeholders.

http://www.algalbiomass.org/

http://www.algalbiomass.org/


Minimum Descriptive Language (MDL)


OntoMDL

Advantages of building ontologies from standards

Already built-in-consensus on the use of key domainspecific terminologiesMinimal semantic loss as standards informally include therelationships between concepts and ease of knowledgetransfer.


Scope of OntoMDL


Ontology Development Methodology


Ontology Lifecycle


Ontology Lifecycle: Phases

Guided by the Neon project,InceptionKnowledge AcquisitionAssessmentDesignImplementation

http://www.neon-project.org/

http://www.neon-project.org/


Knowledge Acquisition Phase

An algal production unit can bea newly established plant with no access to knowledgebases from existing plants (Competitive markets can drivethe situation).a newly established plant which has access to and wouldlike to benefit from knowledge bases acquired fromexisting plants.an existing plant which would like to benefit from a wellrecorded history of knowledge bases.


Assessment Phase

Assessment of identified standards, assessing otherontologies identified for reuse.Merging ontologies, reengineering ontologies.Refrain from using NLP techniques in the initial iterations.A detailed perusal of the standards by knowledgeengineers, guided by domain experts, for knowledgeextraction.Iterative evolution of the standards based on the ontologiesdeveloped.

After a few iterations of the standards-ontology mapping, NLPtechniques guided by the lessons learned can be explored.


OntoMDL Conceptualisation

Core ConceptsProcessInput

ProcessOutput



SpecialisationProcess Input

CarbonInputEnergyInputWaterInput



SpecialisationProcess Output

ConstituentProductIndirectProductLiquidWaste


OntoMDL: Additional Conceptualisation

Background KnowledgeAlgalOperationUnit

AlgalOperationProcess


OntoMDL


Working Demo

Building Ontologies for Algal Biomass Operations 2012

Education

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