Semantic Grid Tools for Rural Policy Development & Appraisal

1. Semantic Grid Tools for RuralPolicy Development & Appraisal Department of Computing Science, University of Aberdeen Department of Geography & Environment, University of Aberdeen Macaulay Institute, Aberdeen

2. Outline • eSocial Science & The Grid • The Semantic Grid • PolicyGrid – Aims & Activities • Supporting Social Simulation • Metadata Challenges for eSocial Science • Supporting Argumentation • Summary

3. eSocial Science & The Grid • eScience • UK DTI characterises as distributed global collaborations enabled by the Internet. • The concept of the Grid promises to provide access to large data collections, near unlimited processing resources for running experiments and studies, and advanced visualisation facilities. • Grid Components • Computational grid • (Scavenging grid) • Data grid

4. The Semantic Grid • Semantic Grid • A vision of eScience infrastructure in which there is much richer support for researchers to publish, share and re-use resources, integrate heterogeneous information, collaborate, access decision support tools, etc. • Central to this view is the integration of Grid technologies with Semantic Web technologies. • RDF Resource Description Framework • OWL Web Ontology Language

5. Agents Smart portals Data mining Social networking Smart search Knowledge Discovery Information Integration and aggregation The Semantic Grid CourtesyCarole Goble, University of Manchester

6. Ontologies

7. PolicyGrid • Aims • To facilitate evidence-based rural, social, and land-use policy-making through integrated analysis of mixed data types; • To demonstrate that Semantic Web/Grid solutions can be deployed to support various facets of evidence-based policy-making through the development of appropriate tools; • To focus on the authoring of relevant ontologies to support rural, social and land-use policy domains; • To investigate issues surrounding communication of semantic metadata to social scientists and policy practitioners; • To promote awareness of the Semantic Grid vision and supporting technologies amongst social scientists. • Builds upon work of the earlier Fearlus-Gpilot demonstrator project.

8. PolicyGrid • What are the methodological drivers behindour activities? • A myriad of policy evaluation challenges facingcontemporary social scientists; • Increased focus on methods and tools for integrated policy evaluation; • Increased emphasis on multi-method or mixed-methods approaches to evaluation, where emphasis is placed on plural types and sources of data; • Diverse epistemological approaches and analytical techniques. • A key driver - evidence-based policy making – a mantra often summarised as meaning ‘what matters is what works’ (Cabinet Office, 1999).

9. Supporting Social Simulation • Fearlus Land-Use Model Case Study • Aims • To serve a well-established simulationframework to the wider community • To support collaboration among socialscientists by providing a sharedco-laboratory environment forexperimentation. • Achievements • Distributed simulation experiments run across Grid nodes. • Simulation results annotated with metadata (RDF). • Users can publish and share simulation modelparameters and re-run experiments. • Support for creation of hypotheses, arguments. • Ontology to support annotation of simulation resources.

10. Simulation Parameters @begin environmentType Toroidal-Moore neighbourhoodRadius 1 climateBSSize 0 economyBSSize 16 landParcelBSSize 0 nLandUse 8 pLandUseDontCare 0.0 clumping None envXSize 15 envYSize 15 nSubPops 2 strategyChangeUnit 0.0 neighbourNoiseMax 0.0 neighbourNoiseMin 0.0 breakEvenThreshold 8 landParcelPrice 16 subPopFile subPopDesc.sd suddenchange150clim 0000000000 0001000000 0000000001 0000010000 0000000000 1111110111 1111111111 1111111101 1110111111 1111101111 1111011111 @begin NumberOfStrategyClasses: 3 Class AboveThresholdProbability BelowThresholdNonImitativeProbability BelowThresholdImitativeProbability InitialProbability HabitStrategy 1.0 0.0 0.0 0.0 RandomStrategy 0.0 1.0 0.0 1.0 NoStrategy 0.0 0.0 1.0

11. Architecture WEB/GRID SERVICES FEARLUS MODEL INTERFACE FEARLUS Model <INTERFACE> FEARLUS OGSA 3.2.1 Desktop Application FEARLUS Experiment Service MODEL 0-6-5 <CLASS> Upload Service Model Factory <INTERFACE> META-DATA Repository Service JDBC4ELDAS My SQL ELDAS Data Access Service My Workspace Web Interface Public Repository (Longwell) Web Interface

12. My Workspace

13. Simulation Workflow Support • Allows scientists to describe and enact their experimental processes in a structured, repeatable and verifiable way. Taverna workflow tool

14. MetaData Challenges for eSocial Science • Ontological Approach: • Universally shared conceptualisation of a domain of discourse. • Provides a controlled vocabulary. • How to capture fuzzy/vague concepts? • sustainability, accessibility, poverty … • How to make different conceptualisations of a domain of discourse co-exist? • Differences in granularity. • Inconsistent points of view. • Meaning is often fluid, contextual. There will never be just one ontology! [In social science or any other activity]

15. Place Political Office Country Country City Annotations - Semantic Web View • NVivo Annotation - assert facts usingterms (metadata in RDF). Represent terms and theirrelationships (ontology in OWL). Annotations help to connectWeb resources.

16. Annotations - Qualitative Social Science View • Qualitative data analysis tools such as NVivo. Can we combine the Semantic Web view withthe qualitative analysis approach?

17. Folksonomies - A Solution for eSocial Science? • Ontologies are often seen as a “top-down” solution. • Will the social science community accept this? • Folksonomy • Derivation: “folk” + “taxonomy” • Collaboratively generated, open labelling system. • Social networks and collective intelligence. • Power derived from community “buy-in”. • Problem of meta-noise…

18. Folksonomies - A Solution for eSocial Science?

19. Folksonomies - A Solution for eSocial Science?

20. Supporting Argumentation

21. Arguments & Evidence

22. PolicyGrid Team • Project Investigators • John Farrington (Geography & Environment) • Gary Polhill, Nick Gotts (Macaulay Institute) • Pete Edwards, Alun Preece, Chris Mellish(Computing Science) • Project Staff • Abdelkader Gouaich, Feikje Hielkema,Edoardo Pignotti, ChuiChing Tan www.policygrid.org