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Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
Realising the Potential of Algal Biomass Production   through Semantic Web and Linked data
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Realising the Potential of Algal Biomass Production through Semantic Web and Linked data

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Presentation at I-Semantics 2012, Graz

Presentation at I-Semantics 2012, Graz

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  • 1. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazRealising the Potential of Algal BiomassProduction through Semantic Web and Linked data The LEAPS Framework Monika Solanki Knowledge Based Engineering Lab Birmingham City University, UK Joint work with Johannes Skarka Karlsruhe Institute of Technology, ITAS
  • 2. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazOutline1 Motivation2 Modelling Algal Biomass Knowledge3 Lifting XML datasets to Linked data4 System Architecture5 Querying Linked Algal Biomass Data6 Conclusion and Future work
  • 3. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, Graz Motivation
  • 4. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazAlgal biomass as biofuels Extensive research* is being undertaken in the search and production of naturally viable and sustainable energy sources. The idea that algae biomass based biofuels could serve as an alternative to fossil fuels has been embraced by councils across the globe. Major companies, government bodies and dedicated non profit organisations* are getting involved. The domain is a rich source of data/information/knowledge. *http://www.algalbiomass.org/ *http://www.eaba-association.eu/ *http://www.enalgae.eu/
  • 5. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazAlgal biomass as biofuels: Observations No systematic analysis of the algae biomass potential for North-Western Europe. Most of the knowledge buried in various formats of images, spreadsheets, proprietary data sources and grey literature. Lack of a knowledge level infrastructure that is equipped with the capabilities to provide semantic grounding to the datasets for algal biomass. Low levels of motivation among stakeholders, for datasets to be interlinked, shared and reused within the biomass community.
  • 6. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazLEAPS: A Potential SolutionLinked Entities for Algal Plant Sites motivate the use of Semantic Web technologies and LOD for the algal biomass domain. laying out a set of ontological requirements for knowledge representation that support the publication of algal biomass data. elaborating on how algal biomass datasets are transformed to their corresponding RDF model representation. interlinking the generated RDF datasets along spatial dimensions with other datasets on the Web of data. visualising the linked datasets via an end user LOD REST Web service.
  • 7. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazEnAlgae: 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/
  • 8. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazEnAlgae: 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/
  • 9. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazSW and Linked data for Algal Biomass Algal biomass data manifests itself across several facets. The value/supply chain ranges from cultivation of algae to production of biofuels and other products. Cultivation, harvesting, processing and fuel production further involves several intermediate processes. Every stage in the algal supply chain is governed by requirements, regulatory policies and strategies. Each of the facets consumes and produces a large volume of unstructured data and information.
  • 10. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazAlgal Supply Chain
  • 11. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazSW, Linked data and the Algal Supply Chain
  • 12. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazCompetency questions for stage 1 datasetsData driven Which are the algal operation sites with CO2 sources that have CO2 emissions less than 130000 kgs, where total costs of supplying CO2 is lower then 5000 GBP per ton of CO2 , areal yield is greater than 30 tons per hectare and which are located within the NUTS region “UKM61”? Supplement the data with supporting information about the region. Which are the top ten algal operation sites with the lowest impact on global warming potential? For a given algal operation site which are the first five most cost effective combinations of light, water, nutrients and CO2 sources?
  • 13. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazModelling Algal Biomass Knowledge
  • 14. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazOntological requirementsOntologies needed to represent Spatiality: location of possible algae cultivation sites, location of the sources of consumables (CO2 , nutrients and water). Geometries: area of the cultivation site - extents, polygons, linear and ring arrays. Units and Measurements: conventional measurement units such as Kgs for quantities and hectares for area, bespoke units of measurements, i.e., Kgs/hectare or Kgs/annum. Territorial units for statistics: core concepts of the NUTS system. Domain specific knowledge: algae cultivation sites, CO2 sources, pipelines.
  • 15. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazOntologies for Algal Biomass: Reuse Spatial Data: WGS84, spatial relations, Geonames, NeoGeo Geometries: WGS84, extended NeoGeo. Units and Measurements: extended QUDT http://www.w3.org/2003/01/geo/wgs84_pos http://www.ordnancesurvey.co.uk/oswebsite/ontology/ spatialrelations.owl http://www.geonames.org/ontology/ontology_v2.2.1.rdf http://geovocab.org/geometry http://qudt.org/1.1/vocab/dimensionalunit
  • 16. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazOntologies for Algal Biomass: Reuse
  • 17. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazOntologies for Algal Biomass: Domainknowledge Ontologies for modelling spatial knowledge, units and measurements were reused. Discovering vocabularies conceptualising the domain knowledge for algal biomass was non trivial. Concepts and relationships for algal biomass had to be defined from ground-up in accordance to the principles of ontology development The design was very strongly guided by feedback from questionnaires made available to the stakeholders, interviews with domain experts, providers of raw datasets and grey literature from the algal biomass and biofuels domain.
  • 18. Ontologies for Algal Biomass: Domainknowledge Ontologies available at http:/purl.org/biomass/ontologies
  • 19. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazDesigning URIs for Algal Biomass Data
  • 20. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazLifting XML datasets to Linked data
  • 21. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazLifting XML datasets to Linked dataRaw data
  • 22. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazLifting XML datasets to Linked dataFirst step The first part of the data processing and the potential calculation are performed in a GIS-based model which was developed for this purpose using ArcGIS. Raw datasets with various origins and formats - transformed using bespoke computational algorithms to an ArchGIS specific XML format. brings uniformity in the format of representation of the datasets and in the process of transformation. important computations that are part of the final datasets are performed.
  • 23. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazLifting XML datasets to Linked dataSecond step The original data sources had several limitations and a one-to-one transformation was not possible. The XML data sources related the biomass production sites and the CO2 sources via the pipeline dataset. In order to query for all sources that supplied CO2 to a specific site, the query would have to be made via the pipeline dataset. The site, source and NUTS identifiers in the datasets were string literals rather than URIs. A bespoke parser that exploits XPath to selectively query the XML datasets and generate linked data was implemented. It utilises a complex underlying data structure to facilitate the transformation.
  • 24. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazLifting XML datasets to Linked data Four datasets were transformed and stored in distributed triple store repositories. The NUTS regions dataset in RDF was available but there was no SPARQL endpoint or service to query the dataset. We retrieved the dataset dump and curated it in our local triple store as a separate repository. The transformed datasets interlinked resources defining sites, CO2 sources, pipelines, regions and NUTS data.
  • 25. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazLifting XML datasets to Linked data
  • 26. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazSystem Architecture
  • 27. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazSystem Architecture
  • 28. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazArchitecture: Main components Parsing modules: lifting the data from their original formats to RDF. Ontologies. Linking engine: producing the linked data representation of the datasets. Triple store: OWLIM SE 5.0. REST Web services. SPARQL endpoints. Web Interface.
  • 29. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazQuerying Linked Algal Biomass Data Most queries over the datasets are based on retrieving knowledge centered around location information. The queries are federated across the various repositories holding the linked data. Representative Query:Which are the algal operation sites with CO2 sources that haveCO2 emissions less than 130000 kgs, where total costs ofsupplying CO2 is lower then 5000 GBP per ton of CO2 , arealyield is greater than 30 tons per hectare and which are locatedwithin the NUTS region “UKM61”? Supplement the data withsupporting information about the region.
  • 30. Typical QueryWHERE { SERVICE <http://localhost/repositories/biomass> { ?site a site:OperationSite; site:inNUTSRegion ?region; geo:location ?loc. ?loc geo:lat ?lat. ?loc geo:long ?long. ?site site:hasSiteID ?siteID; site:hasArealYield ?z. ?z qudt:quantityValue ?y. ?y qudt:numericValue ?arealYield. ?y qudt:unit ?unit. } SERVICE <http://localhost/repositories/co2source> { ?source a co2:CO2Source; co2:hasSourceID ?sourceID; co2:hasCO2Emission ?emission. ?emission qudt:quantityValue ?emissionQty. ?emissionQty qudt:numericValue ?emissionValue. } continued...
  • 31. Typical Query SERVICE <http://localhost/repositories/pipeline> { ?pipe a pipe:Pipeline; pipe:hasSiteID ?siteID; pipe:hasSourceID ?sourceID; pipe:hasTotalCO2Cost ?cost. ?cost qudt:quantityValue ?qty. ?qty qudt:numericValue ?totalCO2CostValue. ?qty qudt:unit ?totalCO2CostUnit. } SERVICE <http://localhost/repositories/region> { regionID a ramon:NUTSRegion; owl:sameAs ?related } FILTER((?emissionValue < 130000) && (contains(str(?region), "UKM61")) && (?arealYield > 30) && (?totalCO2CostValue < 5000))}
  • 32. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazRelated Efforts,Conclusions and Future Work
  • 33. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazRelated efforts AquaFuels*: a taxonomy of algal strains available as PDF. BioEnerGIS *: a GIS based Decision support tool, BIOPOLE, for biomass plants feeding district heating systems. BioKDF *: Bioenergy knowledge discovery framework from the U.S. department of Energy. Reegle *: various energy related datasets as linked open data and a SPARQL endpoint to access the datasets. *http://www.aquafuels.eu/ *http://www.bioenergis.eu/ *https://bioenergykdf.net/ *http://data.reegle.info
  • 34. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazConclusions Investigations into using algal biomass as an alternative source of fuel is gaining widespread momentum. The Algal biomass community currently does not employ any knowledge representation techniques to formalise and structure valuable knowledge harnessed through their operations. As research in the sector progresses, a wealth of information will be available that could be exploited by domain specific applications.
  • 35. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazSummaryThe LEAPS framework exploits SW and LD for the algalbiomass community, enabling the screening of data for promising individual plant sites and provides base data for more detailed planning purposes. proposing a set of domain specific ontologies for algal plant sites, CO2 and pipelines to be shared and extended by the community. defining a linked data publishing architecture that transforms raw data in disparate formats to a uniform XML representation. using a set of well established and domain specific ontologies as metadata to transform it further into linked data. providing various data access options such as a SPARQL endpoint, an interactive Google map interface and a REST API for making the data accessible to stakeholders.
  • 36. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazFuture Work Several other datasets need to be integrated once they become available. One of the core datasets - algal strains from Algaebase*. Multifaceted visualisation of the integrated datasets to facilitate the uptake of the framework by stakeholders. Rule based reasoning to model and inference domain specific constraints. *http://www.algaebase.org/
  • 37. monika.solanki@bcu.ac.uk I-Semantics 2012, 5th September 2012, GrazMany Thanks!!!

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