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SemDat: Web-Based Translation Service for Data Classification Systems

SemDat is an interactive online tool designed to assist in translating and classifying data for various systems and taxonomies. The service aims to bridge semantic gaps and enhance decision-making processes using meaningful concept relationships. With a user-friendly interface, SemDat supports the creation of ontologies and offers a flexible approach to analyzing data. Dive into our platform for a comprehensive understanding of its capabilities and benefits.

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SemDat: Web-Based Translation Service for Data Classification Systems

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  1. SemDat: A Web-Based Interactive, Flexible Translation Service for Classification Systems and Taxonomies Center for eResearch & School of Environment University of Auckland William R. Smart SinaMasoud-Ansari Brandon Whitehead Tawan Banchuen Mark Gahegan

  2. Overview • Problem and motivation • A quick tour • Ontology creation • Web app architecture • More snapshots/live demo (perhaps)

  3. Motivation Kyoto Treaty | Kyoto Protocol carbon credits Landcare’s desire to support interoperable data Subset of PhD research

  4. background data schemas and… • Land Cover Data Base (LCDB) • EcoSat • Land Use and Carbon Analysis System (LUCAS)

  5. backgroundLCDB • Three iterations • LCDB1 • LCDB2 • LCDB1.1 (or, LCDB1 second edition) • Primarily for reporting on changes to land cover (1 ha. min. mapping unit) Source: Ministry for the Environment, 2004

  6. backgroundEcoSat • Maps ecosystem attributes from satellite • Regional scale – min. mapping unit 15m • World leader in methods for removing the effect of topography from satellite imagery

  7. backgroundLUCAS • Team housed at MfE • Tasked with developing methods to meet the requirements of the Kyoto Protocol • Goal is to track and quantify changes in New Zealand land use from 1990 to 2008

  8. The specific problem we are solving • We have legends with no spatial data • ... for which we want the full map • For example, the Kyoto Protocol • Worth a lot to have a classified map of NZ with the Kyoto Protocol classes as its legend

  9. are they compatible? • Would an understanding of the semantic structure of each concept in each data store surface meaningful concept relationships? • Would meaningful concept relationships be helpful to decision makers? • Would meaningful concept relationships enhance our understanding of New Zealand’s carbon footprint?

  10. http://semdat.bestgrid.org

  11. http://semdat.bestgrid.org

  12. http://semdat.bestgrid.org

  13. https://wiki.auckland.ac.nz/display/knowcomp/SemDat+Users+Manualhttps://wiki.auckland.ac.nz/display/knowcomp/SemDat+Users+Manual

  14. how? • Workshop! • Invite experts from each respective data source • Share concept development process (pitfalls, concrete and fuzzy concepts, etc.)

  15. An example: LCDB1 and LCDB2(Land-cover database versions 1, 2(or 1b)) • LCDB2 • Matagouri • Mixed Exotic Shrubland • Orchard and Other Perennial Crops  • Other Exotic Forest  • Manuka and or Kanuka • Mangrove  • Landslide  • Low Producing Grassland  • Major Shelterbelts  • Pine Forest - Closed Canopy  • Pine Forest - Open Canopy  • Surface Mine  • Tall Tussock Grassland  • Transport Infrastructure  • Urban Parkland/ Open Space  • Sub Alpine Shrubland • Short-rotation Cropland  • Permanent Snow and Ice  • River  • River and Lakeshore Gravel and Rock  • Lake and Pond  • Indigenous Forest  • Built-up Area  • Coastal Sand and Gravel  • Deciduous Hardwoods  • Depleted Tussock Grassland  • Broadleaved Indigenous Hardwoods  • Alpine Gravel and Rock  • Vineyard  • Afforestation (not imaged)  • Alpine Grass-/Herbfield • Dump  • Estuarine Open Water  • Herbaceous Freshwater Vegetation  • Herbaceous Saline Vegetation  • High Producing Exotic Grassland  • Grey Scrub  • Gorse and Broom  • Fernland • Flaxland • Forest Harvested  • Afforestation (imaged, post LCDB 1)  • LCDB1 • PRIM_HORTICULTURAL  • PLANTED_FOREST  • PRIM_PASTORAL  • SCRUB  • URBAN  • TUSSOCK  • MINES_DUMPS  • MANGROVE  • COASTAL_SANDS  • URBAN_OPEN_SPACE  • COASTAL_WETLANDS  • INDIGENOUS_FOREST  • INLAND_WETLANDS  • INLAND_WATER  • BARE_GROUND  • These databases largely come from the same source • Yet, their legends render them incompatible • For instance, we couldn’t easily compare some class between LCDB1 and LCDB2 • We need a mapping

  16. Can we fix it? (yes we can) • LCDB2 • Matagouri • Mixed Exotic Shrubland • Orchard and Other Perennial Crops  • Other Exotic Forest  • Manuka and or Kanuka • Mangrove  • Landslide  • Low Producing Grassland  • Major Shelterbelts  • Pine Forest - Closed Canopy  • Pine Forest - Open Canopy  • Surface Mine  • Tall Tussock Grassland  • Transport Infrastructure  • Urban Parkland/ Open Space  • Sub Alpine Shrubland • Short-rotation Cropland  • Permanent Snow and Ice  • River  • River and Lakeshore Gravel and Rock  • Lake and Pond  • Indigenous Forest  • Built-up Area  • Coastal Sand and Gravel  • Deciduous Hardwoods  • Depleted Tussock Grassland  • Broadleaved Indigenous Hardwoods  • Alpine Gravel and Rock  • Vineyard  • Afforestation (not imaged)  • Alpine Grass-/Herbfield • Dump  • Estuarine Open Water  • Herbaceous Freshwater Vegetation  • Herbaceous Saline Vegetation  • High Producing Exotic Grassland  • Grey Scrub  • Gorse and Broom  • Fernland • Flaxland • Forest Harvested  • Afforestation (imaged, post LCDB 1)  • LCDB1 • PRIM_HORTICULTURAL  • PLANTED_FOREST  • PRIM_PASTORAL  • SCRUB  • URBAN  • TUSSOCK  • MINES_DUMPS  • MANGROVE  • COASTAL_SANDS  • URBAN_OPEN_SPACE  • COASTAL_WETLANDS  • INDIGENOUS_FOREST  • INLAND_WETLANDS  • INLAND_WATER  • BARE_GROUND  • Build a mapping from one to other, or.. • Build an ontology which contains and links them • The mapping will fall out of the ontology naturally

  17. Ontologies • An ontology is stored as a set of triples • Subject predicate object • John hasColour Orange • Some predicates are special • John subClassOf People • John sameAs John • Our mapping could be an ontology directly • LCDB2:River subClassOf LCDB1:InlandWater • There are also some very comprehensive ontologies available that relate many concepts together • eg Sweet • By making our mapping via an ontology we leverage: • Previously identified relationships between general concepts • Inference engines and data stores to hold our mapping

  18. The system LCDB 2 Map 1 Spatial LUCAS Map 2 Spatial Ontology Alignment (Brodaric’s Engine, GIN) Legend Legend Kyoto Legend (there is no map) Hybrid Map LCDB2 Spatial Lucas Legend Kyoto Legend

  19. SNAPSHOTS/LIVE DEMO

  20. Conclusions • Spatial data format is highly standardized • Legends can be also • The SemDat site uses an ontology to relate a given virtual legend and a spatial legend attached to a map. • Any legend well-connected to the ontology may be rendered as the legend of any other map with a legend that is connected to the ontology • The site allows multiple types of download • WMS • WFS • Shapefil • Chinese province – next test case (supports Madarin) • Ola – Workshop at GIScience?

  21. Technology choices • Ontology storage/inference – • Sesame • Good choice • Map server – happy medium • Mapserver for WMS • Fast – mediation via SLD files • Geoserver for WFS/Shapefile • Flexible – mediation via features • Issues with memory yet to be sorted out • Map storage • Both postgis/postgresql and as shapefiles • Found postgis to be about four times slower for WMS • Site • Custom Javascript • OpenLayers (Javascript) for WMS • Server interface • PHP

  22. Questions Tawan Banchuen, PhD t.banchuen@auckland.ac.nz http://wiki.auckland.ac.nz (keyword: knowledge comp) http://jira.auckland.ac.nz (knowledge computing project) NZ eResearch Symposium http://www.eresearch.org.nz Eclipse RAP http://www.eclipse.org/rap

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