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.
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
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
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
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
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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