SemDat: A Web-Based Interactive, Flexible Translation Service for

Classification Systems and Taxonomies

Center for eResearch & School of EnvironmentUniversity of Auckland

William R. SmartSina Masoud-AnsariBrandon Whitehead

Tawan BanchuenMark Gahegan

Overview

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

Kyoto Treaty | Kyoto Protocolcarbon credits

Motivation

Landcare’s desire to support interoperable data

Subset of PhD research

background data schemas and…

• Land Cover Data Base (LCDB)• EcoSat• Land Use and Carbon Analysis System

(LUCAS)

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

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

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

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

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?

http://semdat.bestgrid.org

http://semdat.bestgrid.org

http://semdat.bestgrid.org

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

how?

• Workshop!• Invite experts from each respective data

source• Share concept development process

(pitfalls, concrete and fuzzy concepts, etc.)

An example: LCDB1 and LCDB2(Land-cover database versions 1, 2(or 1b))

• 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 

•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) 

• 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

Can we fix it? (yes we can)

• 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 

•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) 

• 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

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

The systemLCDB 2

Spatial

Legend

LUCAS

Spatial

Legend

Hybrid Map

LCDB2 Spatial

Lucas LegendKyoto

Legend

Kyoto Legend(there is no map)

Map 2Map 1 Ontology Alignment(Brodaric’s Engine, GIN)

SNAPSHOTS/LIVE DEMO

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?

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

Questions

Tawan Banchuen, PhDt.banchuen@auckland.ac.nzhttp://wiki.auckland.ac.nz (keyword: knowledge

comp)http://jira.auckland.ac.nz (knowledge computing

project)NZ eResearch Symposium

http://www.eresearch.org.nzEclipse RAP http://www.eclipse.org/rap