20100427 Earthster Core Ontology

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An Overview of work on the Earthster Core Ontology, a core ontology for Life Cycle Analysis.

An Overview of work on the Earthster Core Ontology, a core ontology for Life Cycle Analysis.

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  • 1. Earthster Core Ontology
    A core domain ontology for Life Cycle Assessment
  • 2. Introductions
    Earthster is a project, led by Greg Norris, to provide free open source solutions supporting Life Cycle Analysis – that is evaluating the environmental and social impacts of products.
    Brian McBride is a consultant, ontologist, developer and general dogsbody who has worked on semantic web technologies since 2000. He was co-chair of the RDFCore WG in W3C and the initiating developer of the Jena Java library.
    Epimorphics is a Semantic Web startup based in the UK specialising in Linked Open Data.
    http://www.earthster.org/
    http://www.epimorphics.com/
  • 3. Menu
    A little bit about LCA
    Why semantic web?
    A fragment of ECO
    A sense of the rest
    The ECO-system
    A little bit on method
    ERF – an application
    Wrapup
    Season to taste with questions and discussion anytime
  • 4. Life Cycle Assessment
    What is a product’s impact on the environment and society?
    In production aka cradle to gate?
    In use – total lifecyle?
  • 5. Life Cycle Assessment
    • Impact assessment is currently a complex, artful process – this description is considerably simplified
    • 6. Define the goals of the assessment
    • 7. Analyse your supply chain
    • 8. Create an inventory of emissions and aggregated along the supply chain
    • 9. Assess the impact of those effects on issues of interest e.g. global warming, human health, ...
    • 10. There are impact assessment methods that define weighted contributions and give a score e.g. Emissions of CO2equivalent for GHG or DALYs for human health.
  • Earthster Aim:Publish LCA Data on the Web
    To inform folks about the sustainability implications of the decisions we make
    At all points in the supply chain:
    Simple measures for the consumer
    Decision support for the industrial buyer
    Detailed information for the product/process designer
    Enable consumer pressure to flow back down the supply chain to improve the sustainability characteristics of products
  • 11. Why Semantic Web?
    Effects
    Publish the data on the web makes it most freely available – and that is what the semantic web is for
    Linking to other data sources – that is what Linked Open Data is for
    Find an alternative pesticide that targets the same class of organism
    Link to Good Relations data
    Operations
    Interoperability
    Harmonize competing systems by defining a common conceptual model
    Harmonize competing vocabularies by defining common reference data
    LCA research is ongoing – the data required is changing over time
    Finding the best available data
  • 12. ECO: A Core Domain Ontology
    Aim is to offer a vocabulary for core concepts in LCA
    Not lists of instances
    Instances are present if they are a logical necessity of the concept
    Core ontology changes when core concepts change not when a new instance is invented
    Trying to express the consensus conceptual model for the field
    Reverse engineering existing file formats and database schemas
  • 13. A Fragment of ECO - 1
    Process
    Model
    Models
    • Distinguish between statements about a process and a model of the process
    • 14. Allows multiple inconsistent descriptions of the process with a contradiction
    • 15. Gives us somewhere to hang model metadata without named graphs or reification
  • A Fragment of ECO – 2
    Process
    Model
    Quantified Effect
    hasQuantifedEffect
    Models
    Quantity
    Effect
    • Effect is a new abstraction not found in current LCA data structures
    • 16. Key feature is that it is aggregatable
    • 17. Abstraction of product flow, waste flow, elementary flow, land use, wages paid, ...
    • 18. Don’t yet know if it will stick with the LCA community
  • A Fragment of ECO - 3
    Process
    Model
    Quantified Effect
    hasQuantifedEffect
    Models
    • Follows the SUMO upper ontology model for quantities and units
    • 19. Adds the notion of an uncertainty distribution – not part of SUMO
    • 20. There is a separate ontology of uncertainty distributions – they are not a logical necessity of the concept of an uncertainty distribution
    • 21. What about the Zero uncertainty distribution – that is a logical necessity
    Quantity
    Effect
    magnitude
    units
    Uncertainty distribution
  • 22. A Fragment of ECO - 4
    Lake
    Water
    SO2
    Quantified Effect
    Quantity
    Elementary Flow
    • Current LCA data structures have a fixed set of possible elementary flows, or at best a fixed set of properties of elementary flows
    magnitude
    units
    Uncertainty distribution
  • 23. A Fragment of ECO - 5
    Lake
    Water
    SO2
    Quantified Effect
    Quantity
    Elementary Flow
    • ECO allows for more flexibility than that e.g. Could specify the size of the lake into which they emissions occur or provide some information about the kind of fish that live in it
    • 24. Should we have allowed that extension:
    • 25. giving in to the temptation to ‘improve’ the current shared conceptual model
    • 26. done in a way that extends the legacy and does not contradict it
    • 27. extensibility is a key advantage of the approach
  • Other things in ECO
    Non-elementary flows – products through the supply chain
    Supply chain connections
    Allocations – for processes with multiple outputs
    Attributes – what percentage of inputs are certified organic?
    Impact assessments
    Expressions and variables
    Markets and other aggregations – e.g. NA Electricity
    Statistical and IO data
    Annotations and other metadata
    Scale: 66 classes, 81 properties, 7 individuals
  • 28. Other Parts
    OWL Full axioms
    Mostly in OWL DL – only a few FULL axioms
    Bridging ontologies
    To SUMO, FOAF, TIME, GoodRelations, ...
    Mimimize ontological commitment
    Extension ontologies
    FASC, distributions, file formats, attributes, impact assessments, flow reference data, ...
  • 29. Documentation
    How much fun is it trying to understand a mid-to-large ontology from an OWL file?
    design choices and reasons
  • 39. Scope and Purpose
    There is no such thing as the perfect ontology for a domain
    The ontologist has to make modelling choices
    Those choices are influenced by the scope and purpose of the ontology
    • Rings of focus model
    • 40. ring 1 : model in detail
    • 41. ring 2 : model lightly
    • 42. ring 3 : aiming for no discontinuities
    • 43. ring 4 : don’t care
  • Status
    Study the domain
    Talk to domain experts
    Read
    Study existing systems
    Put together version 0.1
    Try it out on a test application or two
    Put together version 0.2
    Socialise it with leading community members
    Put together version 0.3
    Seek wider community input
  • 44. Earthster Reference Flows
    Problem: Current databases and impact assessment methods use different vocabularies
    Relevant effects get missed from impact assessments
    Duplicate effects get ‘invented’
    Hard: if the method specifies paraquat ion and you have numbers for paraquat
    Are they equivalent?
    Is there a fudge factor?
  • 45. Replace lists with structure
    Currently LCA databases work with lists of elementary flows and give them Ids
    ECO has structure for these elementary flows:
    Flowable
    Energy – use source + density to identify
    Substance
    Chemical – use CAS numbers to identify
    Isoptopes – use CAS number + atomic weight
    Ores – use mix of substances
    Other – adhoclist – bring in a structure if one emerges
    Compartment
    taxonomy
    Time frame
    These form a taxonomy
    Using Skos rather than class structure to stay flexible
    With an extensible set of modifiers
  • 46. Aim ...
    Much smaller lists to manage
    Manage modifier attributes instead
    Ability to extend the list of flows without ambiguity
    Automated matching of legacy flows to the structure
    Involve the community
    Is there a fudge factor?
    Publish reference data on the web
    Crowd source corrections/updates
    Relate different flows in different vocabularies through subsumption
    Offers a shared resource for defining a vocabulary – that allows competition with interoperability
  • 47. Summary
    Making better decisions from a sustainability perspective – that’s an important problem
    Open sharing of information enables market pressure to operate
    Field
    Somewhat fragmented
    Evolving
    Information rich – the value is in the data
    Codifiable knowledge
    Links to other data has economic value
    Good space for using Semantic Web technology
  • 48. Image Attributions
    Hot, flickr:judepics
    Industry, flickr:hans s
    Crushed by the Wheels of Industry,flikr: ProblemKind
    Hong Kong supermarket in Chinatown,flikr: vauvau
    Crushed by the wheels of Industry 2, flikr:Problemkind
    Barn Owl, flickr:AviaVenefica
  • 49. Image Attributions
    Offshore Windfarm Turbine, flicr:phault
    Macro Leaf, flickr:seeks2dream
    Spaghetti al burro, flickr:cesarastudillo
    Stop, flickr:active metabolite
    On Target, flickr:viZZual.com
  • 50. Questions? Thoughts? Observations?