1. G-STAR
GeoSemantic Technologies for
Archaeological Resources
Paul Cripps
Postgraduate Research Student, University of South Wales, Faculty of Advanced Technology

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 http://flic.kr/p/69C5QD

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. Archaeological background
Part I

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. 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.  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.  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. 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. 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. Technological background
Part II

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. 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.  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.  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.  “…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.  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.  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. Applications
Part III

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. 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. Temporal Reasoning
 Relative chronologies
expressed as graphs
 Visualisation
 Timelines
 Harris style matrix as
dynamic interface
 Logic
 Inference
 Consistency
 Validation
 Patterns

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. 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. 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. Potential Impact
Thoughts on where next…
Part IV

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. 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. 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. 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.  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:
 www.archaeogeomancy.net
 Contact:
 paul.cripps@southwales.ac.uk
 paul@archaeogeomancy.net
 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.
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