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GeoSemantic Technologies for Archaeological Resources


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The semantics of heritage data is a growing area of interest with ontologies such as the CIDOC-CRM providing semantic frameworks and exemplary projects such as STAR and STELLAR demonstrating what can be done using semantic technologies applied to archaeological resources. In the world of the Semantic Web, advances regarding geosemantics have emerged to extend research more fully into the spatio-temporal domain, for example extending the SPARQL standard to produce GeoSPARQL. Importantly, the use of semantic technologies, particularly the structure of RDF, aligns with graph and network based approaches, providing a rich fusion of techniques for geospatial analysis of heritage data expressed in such a manner.

This paper gives an overview of the ongoing G-STAR research project (GeoSemantic Technologies for Archaeological Resources) with reference to broader sectoral links particularly to commercial archaeology. Particular attention is paid to examining the integration of spatial data into the heritage Global Graph and the relationship between Spatial Data Infrastructure (SDI) and Linked Data, moving beyond notions of ‘location’ as simple nodes, placenames and coordinates towards fuller support for complex geometries and advanced spatial reasoning. Finally, the potential impacts of such research is discussed with particular reference to the current practice of commercial archaeology, access to and publishing of (legacy, big) data, and leveraging network models to better understand and manage change within archaeological information systems.

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GeoSemantic Technologies for Archaeological Resources

  1. 1. G-STAR GeoSemantic Technologies for Archaeological Resources Paul Cripps Postgraduate Research Student, University of South Wales, Faculty of Advanced Technology
  2. 2. 1. Introduction 2. Archaeological background 1. Places, People, Events, Stuff 3. Technological background 1. The Giant Global Graph & Linked Data 2. Geosemantics 3. GISc & Spatial Data Infrastructures (SDI) 4. Applications 1. Discovery/Search/Retrieval & mediation 2. Reasoning 5. Impact 1. Commercial Sector 2. Academic 3. Public Sector Overview Earthorama by spdorsey
  3. 3. Introduction  G-STAR – doctoral research  Strands:  Semantic Web & Linked Data  Geospatial Semantics  Networks & Graphs  Geographic Information Science  Focus:  Archaeological fieldwork data  Archaeological Inventories
  4. 4. Archaeological background Part I
  5. 5. Heritage Data  Complexity  Multi-vocality, assertion, inference  Rich  Text, images, maps, plans, scientific data, measurements  Often non-digital  Narrative: not data driven  Inherently spatio-temporal  Issues with existing data structures for digital data  Often semantically unclear/ambiguous  Serious risk of carrying these through into Linked Data arena!
  6. 6. Formalised Heritage Data  Some degree of formalisation is essential  Everything can be described as a series of inter-related events  Events may have involved people and things  Events have spatial and temporal bounds  Represent heritage data in terms of who, what, where, when  Using semantically robust data entities: people, places, events, stuff
  7. 7.  Temporal component of data central to archaeology  Who, what, where, when  Relative and absolute chronology  Relative chronologies inferred through spatial relationships (stratigraphy) & finds  Absolute chronologies applied through scientific dating (where possible)  NB probability Temporal Data in Archaeology
  8. 8.  Spatial component of data central to archaeology  Who, what, where, when  Context is crucial; fundamental unit of field recording  Positive Contexts – deposits, fills, layers  Negative Contexts - cuts  Common to all recording systems in use in UK commercial, academic, community and public sector archaeological fieldwork  May have different meanings eg more general concept akin to provenance in classical archaeology Spatial Data in Archaeology
  9. 9. Spatio-temporal Data in Archaeology  Context as Place  Tertiary fill of posthole  Cut of ditch  Foundation wall  Spatial entities  Deposition as Events  Context formation  Finds deposition  Possible to model depositional processes as networks of events
  10. 10. Spatio-temporal Data in Archaeology  Archaeological inventories  Monuments  Time depth  Persistence  Re-use  Built up as interpretive layers from sources including fieldwork  Classes, Dates  Possible to model inference chains and interpretation processes as networks of events
  11. 11. Technological background Part II
  12. 12. Frameworks & Standards  Growing interest in interoperability, resource discovery, digital archives, etc  Metadata and data standards  UK Gemini, FISH, Medin, EHKOS, MidasHeritage, Linked (Open) Data  Spatial Data Infrastructures (SDI)  Inspire directive  Effective & efficient use & re-use
  13. 13. The Semantic Web  The Giant Global Graph  aka Semantic Web  aka Linked Data  Framework for developing truly interoperable (heritage) information systems  Proliferation of Linked Data on the web  Geosemantics now forms part of this
  14. 14.  CIDOC Conceptual Reference Model (CRM)  Core ontology for heritage  Now supported by related national heritage standards eg MidasHeritage  Revelation & Ontological Modelling Project  English Heritage Projects  Focussed on archaeological data as used by EH CfA  Resulted in CRM-EH extensions to the CIDOC CRM Semantic approaches in heritage
  15. 15.  STAR, STELLAR and SENESCHAL  University of South Wales led projects  Collaborations  English Heritage (STAR, STELLAR, SENESCHAL)  Archaeology Data Service (STELLAR, SENESCHAL)  Wessex Archaeology (SENESCHAL)  Building on CRM-EH  Providing tools and demonstrators  Datasets as RDF using CRM-EH  SKOS representation of thesauri  Faceted browsing and search tool demonstrators Semantic approaches in heritage
  16. 16.  “…research area combining Geographic Information Science (GIScience), spatial databases, cognitive science, Artificial Intelligence (AI) and the Semantic Web”  Janowicz, K. et al., 2012.  Relatively new research area Geospatial Semantics
  17. 17.  Numerous Linked Data projects (and growing!)  Typically representing spatial component using named locations (ie appellations, labels) stored as text  May have attached coordinates for known locations  Basic numeric operations eg Bounding Boxes  Visualisation  Limited use of map projections & coordinate systems  Working with place in a largely non-spatial manner  Networks vs cartesian space  Fine for many applications (eg dots on small scale maps) but limited/restrictive for other uses eg excavation data Linked (Spatial) Data
  18. 18.  Publication of the GeoSPARQL standard  Extends SPARQL fully into the spatial domain  Works with higher order geometries; lines, polygons, etc  Spatial queries & operators for RDF building on SPARQL  Integration of CIDOC CRM + GeoSPARQL  Hiebel & Doerr 2013  Integration of RDF + WFS through ‘semantic enablement layers’ on top of geo stack  Janowicz, Keßler, et al. 2009; Janowicz, Schade, et al. 2009  Alignment of SDI with Linked Data Linked (Spatial) Data
  19. 19. Applications Part III
  20. 20. Query mediation & Spatial Searches  Improved approaches to ‘spatial’ indices stored as text  The good old ‘County’ field  True spatial searches & spatial reasoning  Using GeoSPARQL, WFS  Mediated/enhanced by means of geo-ontologies  Providing user with feedback to inform/improve search criteria  Disambiguates complexity of modern geopolitical boundaries
  21. 21. Query mediation & Spatial Searches  Complex geometries as appellations  Places can have depiction(s)  Pass geometry as query parameter to access Linked Data  More flexible  Allows user to specify explicitly which feature to use  my County depiction vs destination text index of County vs destination depiction of County
  22. 22. Temporal Reasoning  Relative chronologies expressed as graphs  Visualisation  Timelines  Harris style matrix as dynamic interface  Logic  Inference  Consistency  Validation  Patterns
  23. 23. Spatial Reasoning  Record enhancement through inference  If we know a location, we can infer ‘within’ etc  Replacement for static, manual ‘spatial’ indexes  No more ‘county’ field!  Relatedness  Add missing spatial relationships  Patterns
  24. 24. Understanding archaeology  The archaeological process as a network of events  Putting archaeologists at the heart of data  Change management & revision  Interpretations change  Typologies/schema revised  Knock on effects; propagation  Track assertions  Leverage power of Linked Data
  25. 25. Integration  Linked Data resources  Finds from fieldwork become museum objects  Features from fieldwork become inventory records  Enormous research potential for Linked (geo) Data  Further enhanced if semantically enabled Linked Data
  26. 26. Potential Impact Thoughts on where next… Part IV
  27. 27. Overview  Distributed approach to data  Organisations curate & publish data they create  Digital repositories eg ADS as Linked Data publishers  Linked Data approach provides decentralised architecture for dynamic access  Semantic layer (eg CIDOC CRM, CRM EH) provides ‘glue’ for heterogeneous sources  Tiered access  Not Linked Open Data!  Licensing, access constraints  Leverage generic network & graph based tools, interfaces, etc building on RDF  Esp. visualisation  Improved efficiency  Emphasis on data vs grey literature ‘paper’ reports  Less duplication/conflict between datasets  Easier to manage/maintain nationally  Local expertise
  28. 28. Commercial Archaeology  Need for efficient records management  Need to track change through projects  Multiple specialists  Long durations  Dissemination, reporting, archive  Accessibility & re-use  Access to resources  Massive cost/time overheads  Data quality
  29. 29. Academia  Better access to semantically enabled Linked Data  Enables integrative research projects  Reduces grant money wasted on (repeated, per project) data collation  Allows academic projects to better link to, inform & be informed by commercial & community projects through common frameworks
  30. 30. Public Sector  Better informed decision making through access to data  Inventory records layered onto source records  Explicit linkages, inference chains, audit  Improved access  Direct access for contractors to semantically enabled Linked Data
  31. 31.  Thanks to:  Wessex Archaeology, University of South Wales, English Heritage  Doug Tudhope, Ceri Binding, Keith May, Andreas Vlachidis, Sarah May, Chris Brayne, Ant Beck  Further information:   Contact:    References  Doerr, M & Hiebel, G. 2013. CRMgeo : Linking the CIDOC CRM to GeoSPARQL through a Spatiotemporal Refinement.  Janowicz, K. et al., 2012. Geospatial Semantics and Linked Spatiotemporal Data – Past , Present , and Future.  Janowicz, K., Schade, S., et al. 2009. A transparent semantic enablement layer for the geospatial web.  Janowicz, K., Keßler, C., et al. 2009. Towards Semantic Enablement for Spatial Data Infrastructures. fin