Center for eResearch & School of Environment University of Auckland William R. Smart

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.)

17. 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

18. 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

19. 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

20. 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

21. SNAPSHOTS/LIVE DEMO

22. 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?

23. 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

24. 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