The document summarizes a presentation about SEA (Semantic Explorer for Archaeology), a framework for interactively querying, visualizing, and analyzing linked archaeological datasets. SEA includes components for building queries, running queries against linked data through SPARQL, visualizing query results and data relationships, performing statistical analysis, and accessing the data through a RESTful API. The goal of SEA is to provide archaeologists an integrated environment for exploring semantic archaeological data.

1. CAA 2011 Beijing
SEA: A Framework for Interactive Querying,
Visualisation and Statistical Analysis of Linked
Archaeological Datasets
Monika Solanki
m.solanki@mcs.le.ac.uk
Department of Computer Science
Joint work with
Yi Hong
Department of Computer Science
Katharina Rebay-Salisbury
School of Archaeology and Ancient History
University of Leicester, UK
Monika Solanki

2. Talk outline CAA 2011 Beijing
Outline
Context: Tracing Networks
Motivation
Case study
Semantic Explorer for
Archaeology
Conclusions and Future work
Demo
Monika Solanki

3. Context CAA 2011 Beijing
Tracing Networks
Investigates the network of contacts across and beyond
the Mediterranean region, between the late bronze age
and the late classical period (c.1500-c.200 BCE) by
interrogating material objects
Seven archaeological case studies fully integrated with
computer science projects
http://www.tracingnetworks.org/
Monika Solanki

4. Context CAA 2011 Beijing
Tracing Networks
Monika Solanki

5. Context CAA 2011 Beijing
Tracing Networks
Archaeologists study a wide range of material objects.
By tracking them at every stage of their production,
distribution, use, and consumption across a large
geographical region, over a long time period, they can
trace the links between the people who made, used, and
taught others to make them.
The Chaîne opératoire
Cross-craft interaction
Monika Solanki

6. Motivation CAA 2011 Beijing
Motivation: Archaeological perspective
Key Barriers to adopting Semantic Web technologies
The most time-consuming part of an archaeological
investigation is the post-excavation analysis.
There is a lack of tools and platforms that provide an
integrated environment for interactive querying,
visualisation and statistical analysis
Traditional search and retrieval mechanisms generally
provided “Google” style keyword search or “Library” style
drop down search.
They assume knowledge of controlled vocabularies,
terminology and structure of the underlying ontological
schemas.
Monika Solanki

7. Motivation CAA 2011 Beijing
Motivation: Computer Science perspective
To increase the uptake and usage of semantically rich
archaeological data, it needs to be openly available and
accessible by humans and applications.
An integrated view of diverse data sources is innovative
and of immense potential value for the archaeological
community.
There is therefore a mileage in combining the task of
archiving, querying and analysing the data within a single
framework.
Archaeological data is fragmentary. Inferencing capabilities
of reasoners can be used to extract implicit knowledge and
contribute to their existing knowledge bases to complete
the fragments.
Monika Solanki

8. Motivation CAA 2011 Beijing
Case study: Human representations
Human representations, identities and social relations in the
Late Bronze and Iron Age of Central Europe
The scope: examining and analysing human
representations on a range of object types and in a range
of materials, such as bronze and pottery.
The project utilises details such as gestures and postures,
dress and associated objects as keys to understanding
how identity and new understandings of society are
communicated.
Raw data is collected through examining objects from
published literature or in museum collections.
Monika Solanki

9. Motivation CAA 2011 Beijing
Human representations
The analysis generates a large volume of data
Along with details of the human representation on objects,
the data recorded also includes images of these objects.
We have developed a vocabulary that defines various
concepts and relationships of interest in the domain of
human representation as captured in these images.
Using the ontology we generated linked datasets from the
raw data.
We are currently linking to DBpedia and Geonames,
however we are also on the lookout for datasets closely
related to archaeology with which we can link in the future.
Monika Solanki

10. Motivation CAA 2011 Beijing
Human representations: Informal queries
Example 1:
“Find images of riders who appear on objects found in Austria
where the altitude of the excavation site is 500 meters above
sea level. I would also like to know the statistical distribution of
the material and the technologies used for the production of
these objects. I would like to visualise the results as a pie chart
and see the distribution of the sites where these objects were
found on Google Earth”.
Monika Solanki

11. Motivation CAA 2011 Beijing
Human representations: Informal queries
Example 2:
“Find all objects which have images of individuals in the orant
gesture who are wearing a triangular dress, earrings and who
carry a vessel on their head, where the vessel is supported by
their left hand. I would also like to know the statistical
distribution of the gender of these individuals according to the
country in which the objects were found. I would like to
visualise the results as a tree map and see the distribution of
the sites where these objects were found on Google Map”.
Monika Solanki

12. SEA: Semantic Explorer for Archaeology CAA 2011 Beijing
Semantic Explorer for Archaeology
A web application
RESTful APIs for programmatically accessing the TN-LOD
cloud
Interactive and global querying of linked datasets
Data visualisations using user defined perspectives
Statistical analysis using bespoke criteria provided by
archaeologists at runtime
Monika Solanki

13. SEA: Semantic Explorer for Archaeology CAA 2011 Beijing
SEA: Architecture
Monika Solanki

14. SEA: Semantic Explorer for Archaeology CAA 2011 Beijing
SEA: Query Component
Query builder, a SPARQL/SQWRL endpoint and an inference
engine
Aggregates the input data as RDF
triples
Generates several sub queries each
of which correspond to a specific
task
Formalises the query in SPARQL,
includes any constraints
Provides an interface through which
the SPARQL query generated by
aggregating the triples can be edited
Monika Solanki

15. SEA: Semantic Explorer for Archaeology CAA 2011 Beijing
SEA: Query Component
Query builder, a SPARQL/SQWRL endpoint and an inference
engine
Queries can be specified intuitively
Utilises the WordNet dictionary
“Natural Language Query
Summariser”
Records user preferences: statistical
analysis, visualisation
Monika Solanki

16. SEA: Semantic Explorer for Archaeology CAA 2011 Beijing
Building the query using SEA
Monika Solanki

17. SEA: Semantic Explorer for Archaeology CAA 2011 Beijing
Human Representation
“Find images of riders who appear on objects found in Austria where
the altitude of the excavation site is 500 meters above sea level. I
would also like to know the statistical distribution of the material and
the technologies used for the production of these objects. I would like
to visualise the results as a pie chart and see the distribution of the
sites where these objects were found on Google Earth”.
Part 1
Find images of riders who appear on objects found in Austria where
the altitude of the excavation site is 500 meters above sea level.
Part 2
I would also like to know the statistical distribution of the material and
the technologies used for the production of these objects.
Monika Solanki

18. Sub query Part 1
PREFIX tnh:<http://www.tracingnetworks.ac.uk/
ontology/human_representation.owl#>
PREFIX rdf:<http://www.w3.org/1999/02/22-rdf-syntax-ns#>
SELECT ?individual ?object ?site ?country ?abbr ?type
?tech ?image ?altitude ?material
WHERE{
?individual rdf:type tnh:Individual.
?individual tnh:appearOn ?object.
?object tnh:isFoundAtSite ?site.
?site tnh:isLocatedInCountry ?country.
?country tnh:hasCountryAbbr ?abbr.
?object tnh:has1stObjectType thn:rider.
?object tnh:hasImageLink ?image.
?site tnh:hasAltitude ?altitude.
FILTER (?altitude>=500).
FILTER (?abbr="AT").
}
LIMIT 3000

19. SEA: Semantic Explorer for Archaeology CAA 2011 Beijing
SEA: Query Component
Query builder, a SPARQL/SQWRL endpoint and an inference
engine
Includes an option to specify any
reasoning rules.
A rule-based inferencing component
specified to support deductive
reasoning.
SWRL or Jena inferencing rules
used to derive implicit statements
from existing archaeological
knowledge bases
Monika Solanki

20. SEA: Semantic Explorer for Archaeology CAA 2011 Beijing
SEA: Visualiser Component
Three visualisation modules.
Queries generated by the user
Convert the SPARQL triple patterns to GraphML
The visualiser is interactive and allows a user to
expand/collapse nodes in the graph.
Search for a specific node in the graph.
Query Results: linked data, markers on the Google
Earth/Google maps.
Statistical analysis: commonly used statistical analysis
models.
Monika Solanki

21. SEA: Semantic Explorer for Archaeology CAA 2011 Beijing
Visualising the query
Monika Solanki

22. SEA: Semantic Explorer for Archaeology CAA 2011 Beijing
Visualising the query results: Google earth
Monika Solanki

23. SEA: Semantic Explorer for Archaeology CAA 2011 Beijing
Visualising the query results
Monika Solanki

24. SEA: Semantic Explorer for Archaeology CAA 2011 Beijing
SEA: RESTful API
The SEA REST API corresponds to a set of services
simply accessible through HTTP calls.
The SEA API employs content negotiation to decide
whether the result should be encoded in RDF/XML
(default), JSON or plain text.
We have been inspired by the linked data APIs published
by the data.gov.uk.
The APIs do not provide support for PUT/POST request.
They are meant to provide a read only access layer to the
data repositories.
The SEA API layer can also act as a proxy over a SPARQL
endpoint. This allows a user to specify a sparql query as a
query parameter.
Monika Solanki

25. Related work CAA 2011 Beijing
Closely related work
D2RQ: Berlin
Virtuoso: Open Link Software
STAR: Glamorgan, English Heritage
STELLAR: Glamorgan, English Heritage
TRANSLATION: Southampton
Monika Solanki

26. Related work CAA 2011 Beijing
Grand vision: The TN-LOD cloud
Tracing Networks through Linked Open Data
Monika Solanki

27. Conclusions CAA 2011 Beijing
Conclusions
Little work has been done so far in the Semantic web
community that can motivate archaeologists to adopt their
technologies to manage and analysis data.
An exploratory attempt to reconstruct the Chaîne
opératoire using the principles of linked open data.
A transformation framework for migrating large volumes of
archaeological data stored in RDBs to ontology based data
sets on the Semantic Web.
SEA: A unified framework that allows archaeologists with
basic knowledge of Semantic Web technologies to
“explore” their datasets through interactive querying,
visualisation and analysis.
Monika Solanki

28. Future work CAA 2011 Beijing
Future work
Implement a user-friendly graphical modeling environment
for the language in GMF (Graphical Modeling Framework)
to allow easy creation and editing of transformation rules.
Extend the query interface so that it allows archaeologists
to specify ranking heuristics for the search results.
Extend the visualisation interface by providing a faceted
browser that allows the archaeologist to visualise query
results along several facets.
Augment the support provided for inference making.
Keeping a close eye on the linked data cloud for any
relevant archaeological datasets that may eventually be
published so that we can link to it.
Monika Solanki

29. Acknowledgements CAA 2011 Beijing
Acknowledgements
Computer Science
Prof Jose Fiadeiro
Yi Hong
Archaeology
Prof Lin Foxhall
Katharina Rebay-Salisbury
Monika Solanki

30. CAA 2011 Beijing
Many Thanks!!!
Monika Solanki