Semantic Grid Tools for Rural Policy Development & Appraisal Department of Computing Science, University of Aberdeen Department of Geography & Environment, University of Aberdeen Macaulay Institute, Aberdeen

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

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

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

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

Ontologies

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-G pilot demonstrator project.

PolicyGrid What are the methodological drivers behind our activities? A myriad of policy evaluation challenges facing contemporary 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).

Supporting Social Simulation Fearlus Land-Use Model Case Study Aims To serve a well-established simulation framework to the wider community To support collaboration among social scientists by providing a shared co-laboratory environment for experimentation. Achievements Distributed simulation experiments run across Grid nodes. Simulation results annotated with metadata (RDF). Users can publish and share simulation model parameters and re-run experiments. Support for creation of hypotheses, arguments. Ontology to support annotation of simulation resources.

Simulation 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 NumberOfStrategyClasses: 3 ClassAboveThresholdProbability BelowThresholdNonImitativeProbability BelowThresholdImitativeProbability InitialProbability HabitStrategy RandomStrategy NoStrategy

Architecture Desktop Application FEARLUS Experiment Service Upload Service Repository Service ELDAS Data Access Service MODEL FEARLUS Model Model Factory FEARLUS META-DATA My Workspace Web Interface Public Repository (Longwell) Web Interface My SQL OGSA WEB/GRID SERVICESFEARLUS MODEL INTERFACE JDBC4ELDAS

My Workspace

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

MetaData Challenges for eSocial Science n Ontological Approach: –Universally shared conceptualisation of a domain of discourse. –Provides a controlled vocabulary. n How to capture fuzzy/vague concepts? –sustainability, accessibility, poverty … n 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]

Annotations - Semantic Web View n NVivo Country Political Office City Place Annotation - assert facts using terms (metadata in RDF). Represent terms and their relationships (ontology in OWL). Annotations help to connect Web resources.

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

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

Folksonomies - A Solution for eSocial Science?

Supporting Argumentation

Arguments & Evidence

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