CAA 2014: Geosemantic Tools for Archaeological Research

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Presentation given on the GSTAR project at the Computer Applications and Quantitative Methods in Archaeology conference, Paris, France, 24/04/2014

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  • Cool Paul. We need some time to talk (maybe some beers ;-). Really like where you're going. This work should have a major impact! Keep it up.

    BTW - thanks for the nod
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CAA 2014: Geosemantic Tools for Archaeological Research

  1. 1. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 Geosemantic Tools for Archaeological Research (GSTAR) Paul Cripps University of South Wales, Trefforest, UK • Hypermedia Research Unit • Geographic Information Systems (GIS) Research Unit Archaeogeomancy, Salisbury, UK http://gstar.archaeogeomancy.net/
  2. 2. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 Introduction • Background • GSTAR project • Case Study • Conclusions Earthorama by spdorsey http://flic.kr/p/69C5QD
  3. 3. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 CRM EH  STAR  STELLAR • CIDOC CRM – Conceptual Reference Model • CRM EH – Archaeological extension to CIDOC CRM • English Heritage • STAR – Semantic Technologies for Archaeological Resources • English Heritage • University of South Wales • STELLAR – Semantic Technologies Enhancing Links and Linked Data for Archaeological Resources • English Heritage • University of South Wales
  4. 4. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 CRM EH  STAR  STELLAR • CRM EH: Extension to the CIDOC CRM • Archaeological fieldwork data • Excavation; contexts and stratigraphy • Finds discovery and processing • Analysis and Interpretation • Based on English Heritage Context Recording System • STAR: Developed infrastructure for working with CRM EH including demonstrators • STELLAR: Developed tools for working with STAR outputs • STELLAR Toolkit • Inputs: structured data in any schema • Mapping via built in and user defined templates • Outputs: Linked Data compliant with CIDOC CRM and CRM EH
  5. 5. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 SilburyHillLinkedData English Heritage, Archaeology Data Service Linked Data resource built using STELLAR Toolkit
  6. 6. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 ColonisationofBritainLinkedData Wessex Archaeology, Archaeology Data Service Linked Data resource built using STELLAR Toolkit including Ordnance Survey Open Data
  7. 7. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014  GSTAR • Aims & Objectives: • To incorporate complex geospatial information into our ontological models • Vector depictions: lines, polygons • Investigate advances in geospatial and geosemantic approaches • Application of geosemantics and Linked Geospatial Data approaches to archaeological resources • Integration of heterogeneous resources via spatial components of heritage data • Application of research questions across diverse heritage resources
  8. 8. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 Geospatial Information • Background: • Archaeological data is inherently spatial • Diverse range of spatial information • Non-spatial data can be related to a spatial component • CRM EH modelled spatial component using specialisations of E53 Place • Stratigraphic Units as Places • Stratigraphic Units have spatial bounds • Positive and Negative Stratigraphic Units • Positive Stratigraphic Units also contain archaeological deposits • Finds discovered in Places • Samples taken from archaeological deposits • Sites, Monuments and Features as Places • Identified from excavation, remote sensing, surveys etc
  9. 9. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 Spatial entities • Features observed and depicted • Post Hole • Stratigraphic units excavated and recorded • Fill of Post Hole • Physical relationships observed • Temporal relationships inferred • Interpretation… • Features grouped and phased to form Sites and Monuments • Post Built Structure • Iron Age Settlement • Legal Designations • Scheduled Monuments
  10. 10. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 InterpretationofexcavationdatausingprojectGIS Wessex Archaeology Spatial component used to aid interpretation through mapping and spatial analysis
  11. 11. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 Stratigraphic Units (aka contexts) • Observed features excavated… • …recorded and interpreted • Stratigraphic Unit = atomic unit of recording • Product of some (pre)historic Event • Deposition of some material  Positive Stratigraphic Unit • Removal of some material  Negative Stratigraphic Unit • Recorded on site: descriptions, classifications, etc • Surveyed on site: hand drawn plans, metric survey
  12. 12. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 RefiningCRMEHtosupportgeospatialclasses Positive and Negative Stratigraphic Units
  13. 13. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 RefiningCRMEHtosupportgeospatialclasses Spatial Relationships
  14. 14. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 Linked Geospatial Data; integrate
  15. 15. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 Story so far… • Literature Review reveals two converging strands of research • Two subject areas within distinct domains: • Semantic Web, Web Science, Linked Data  semantics, location, place, geometry • GIScience  GIS, Spatial Data Infrastructures, web services • Different approaches • Different emphasis • Same ultimate aims • Working with existing data • Channel Tunnel Rail Link (CTRL) data from Oxford Archaeology, archived at the Archaeology Data Service • Spatial data archived as Shapefile • Linked Data available as output from STELLAR
  16. 16. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 Story so far… • Case Study: methods for integrating geospatial and semantic 1. Leveraging geosemantic web approaches: • Triple Stores • SPARQL and GeoSPARQL 2. Leveraging GIScience approaches: • Spatial databases • Web Feature Services (WFS) • Pros and Cons to each • Pure geosemantic approach more ‘integrated’ • But very new; ‘bleeding’ edge… • Performance…? • Hybrid GIScience approach takes advantage of the best bits of each • Stability, robustness • Requires more complex infrastructure • More bespoke, less generic
  17. 17. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 Geosemantic approach • All data stored as triples • RDF, n-triples, XML • Conversion of geospatial data to RDF • CRM EH – Context Depiction (EHE0022) • CRM – Spatial Coordinates (E47) • GeoSPARQL – E47 as geometry • Geospatial data included within semantic data • Well Known Text (WKT) representations of geometries • Very verbose! <owl:Class rdf:about="http://purl.org/crmeh#EHE0022_ContextDepiction"> <rdfs:isDefinedBy rdf:resource="http://purl.org/crmeh#CRMEH"/> <rdfs:subClassOf rdf:resource="http://erlangen- crm.org/110404/E47_Spatial_Coordinates"/> <rdfs:label>Context Depiction</rdfs:label> <rdfs:comment>The Spatial co-ordinates of a Context, defining the actual spatial extent of the context. Usually recorded at the time of excavation or other investigative work </rdfs:comment> </owl:Class>
  18. 18. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 Geosemantic approach • Context Depiction identifies Context • <URI for the context depiction> < URI for p87> < URI for the context> • Context is identified by Context Depiction • <URI for the context> <URI for p87i> <URI for the context depiction> • Context Depiction has type EHE0022 Context Depiction • <URI for the context depiction> < URI for rdf has type> < URI for the type> • Context Depiction has geometry • <URI for the context depiction> < ogc:hasGeometry> < URI for the geometry> • Geometry has type WKT Literal • <URI for the geometry> < ogc:asWKT> < literal = WKT>
  19. 19. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 Geosemantic approach <http://data.archaeologydataservice.ac.uk/10.5284/1000389/EHE0007_249> <http://erlangen-crm.org/101001/P87_is_identified_by> <http://ld.gstar.archaeogeomancy.net/content/crmeh_EHE0022_249> <http://ld.gstar.archaeogeomancy.net/content/crmeh_EHE0022_249> <http://erlangen-crm.org/101001/P87i_identifies> <http://data.archaeologydataservice.ac.uk/10.5284/1000389/EHE0007_249> <http://ld.gstar.archaeogeomancy.net/content/crmeh_EHE0022_249> <http://www.w3.org/1999/02/22-rdf-syntax-ns#type> <http://purl.org/crmeh#EHE0022_ContextDepiction> <http://ld.gstar.archaeogeomancy.net/content/crmeh_EHE0022_249> <ogc:hasGeometry> <http://ld.gstar.archaeogeomancy.net/content/ogc_249> <http://ld.gstar.archaeogeomancy.net/content/ogc_249> <ogc:asWKT> “http://www.opengis.net/def/crs/EPSG/0/27700 POLYGON ((569241.09296391497 169487.76102295844,569242.14972065808 169488.63653720432,569242.96691547381 169489.28166943422,569243.69052183023 169489.80626897677,569244.6163534614 169490.31098325696,569245.4501574568 169490.61867947067,569246.84229549242))"^^ogc:wktLiteral
  20. 20. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 Verbosity… • Underlying DBMS restrictions on field sizes • eg 4K using Oracle • Extended Data Types • Implications for indexing • WKT can be very verbose • Complex features; many nodes • Precision • Spatial precision is key • Not data type precision • Easy to hit the buffers… <http://www.opengis.net/def/crs/EPSG/0/27700>" POLYGON ((569241.09296391497 169487.76102295844,569242.14972065808 169488.63653720432,569242.96691547381 169489.28166943422,569243.69052183023 169489.80626897677,569244.6163534614 169490.31098325696,569245.4501574568 169490.61867947067,569246.84229549242 169491.13088039507,569247.99072701519 169491.45835596515,569249.13880138169 169491.69146708742,569250.70987114881 169491.89583935405,569251.95799411286 169491.83729784336,569251.92909099034 169490.55039028014,569251.44045881392 169490.62534306964,569250.51641985343 169490.64884596801,569249.14214670414 169490.4097076066,569247.53722012346 169490.10687919494,569246.2979823238 169489.74212434812,569245.1707102526 169489.30169979495,569243.96490692452 169488.64508622553,569242.86764335213 169487.49649621252,569242.28527061804 169486.97012858052,569241.09296391497 169487.76102295844))"^^ogc:wktLiteral
  21. 21. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 Hybrid approach • Semantic data stored in triple store • Geospatial data stored in GIS server • Geometries accessed via Web Feature Service (WFS) • Integration achieved by means of WFS URIs • Java middleware • Parsing of input queries • SPARQL queries • WFS requests • Parses results • Uses Geotools libraries for middleware • Handles WFS requests • Handles geometries and geometry collections • Same outputs as geosemantic queries • But leveraging ‘traditional’ GIS spatial functions
  22. 22. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 Stack • Oracle VirtualBox • Virtual machine • Oracle 12c Spatial & Graph • Spatial database, triple store • Jena • Semantic framework • Oracle Weblogic • Web server • Geotools • Java GIS toolkit • Stellar Toolkit • Structured data  Linked Data • Geoserver • GIS server • Eclipse+Maven +JDK • Java programming IDE • ArcGIS, QGIS • Spatial data management • Gruff • Visualisation of Linked Data • Built on AllegroGraph
  23. 23. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 Conclusions • Emerging geosemantic approaches most suitable for integration of geospatial and semantic data • Standards compliant structures • Standards compliant query mechanisms • GeoSPARQL can be integrated within ontologies and Linked Data resources • Simple solution as presented, based solely on W3C/OGC standards • More complex & powerful solutions using CIDOC CRM + GeoSPARQL (eg Heiber & Doerr: CRMgeo) • Some issues eg precision, structures, verbosity • Rapid development in this field • New systems/platforms emerging • Continual improvements; eg imminent next version of GeoSPARQL
  24. 24. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 Acknowledgements • Thanks to: • University of South Wales – funding, supervision, advice • Archaeology Data Service – data from their archives • Wessex Archaeology – data, photographs and images • Wiltshire Council – access to the Historic Environment Record (HER) data • Wiltshire Museums – access to museum collections data • Personal thanks • Supervisors/Advisors: Doug Tudhope, Mark Ware, Alex Lohfink • Research group: Ceri Binding, Andreas Vlachidis, Keith May • Peers and colleagues: Michael Charno, Chris Brayne, Ant Beck, Sarah May, Gerald Heibel, David Dawson • Image Credit • Earthorama by spdorsey http://flic.kr/p/69C5QD
  25. 25. GSTAR – Computer Application & Quantitative Methods in Archaeology 2014 – Paris, April 2014 fin • paul.cripps@southwales.ac.uk • paul@archaeogeomancy.net • @pauljcripps • gstar.archaeogeomancy.net • hypermedia.research.southwales.ac.uk • gis.research.southwales.ac.uk

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