reconstructing the chaine operatoire through semantically linked open data

University of Southampton

Reconstructing the Chaîne opératoire throughSemantically Linked Open Data

Monika Solanki

Department of Computer ScienceUniversity of Leicester

[email protected]

http://es.wikipedia.org/wiki/Archivo:Chaîne_opérato ire.png

Monika Solanki Reconstructing the Chaîne opératoire through Semantically Linked Open Data

http://es.wikipedia.org/wiki/Archivo:Cha�ne_op�ratoire.png

Talk outline University of Southampton

Outline

Tracing Networks

From RDMS to Ontologies

Semantic Explorer forArchaeology

Conclusions and Future work


Tracing Networks University of Southampton

Tracing Networks

investigates the network of contacts across and beyond theMediterranean region, between the late bronze age and the lateclassical period (c.1500-c.200 BCE) by interrogating materialobjects

seven archaeological case studies fully integrated with computerscience projects

programme sets technological networks in their greater social,economic and political contexts to expand our understanding ofwider cultural developments

these networks from the past can help us devise new and moreeffective ways of transmitting knowledge and information in ourdigital world

http://www.tracingnetworks.org/


http://www.tracingnetworks.org/


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 largegeographical region, over a long time period, they cantrace the links between the people who made, used, andtaught others to make them.

The Chaîne opératoire

Cross-craft interaction



Tracing Networks



The Chaîne opératoire

http://www3.hf.uio.no/sarc/iakh/lithic/INTopchain/I NTopchainpaper.html


http://www3.hf.uio.no/sarc/iakh/lithic/INTopchain/INTopchainpaper.html


Tracing Networks



Pertinent QuestionsHow does technical knowledge move from oneperson/group/society to another?

How do people choose which particular knowledge to usefrom the repertoire available?

In what kinds of contexts does innovation appear?



Tracing Networks



Tracing Networks and Semantic web

Archaeology by its very nature focuses on establishinglinkages between past events, places, people and things.

The Semantic Web infrastructure therefore serves as apotential solution because of its emphasis on capturingrelationships and should be exploited to providearchaeological data management solutions.

Little work has been done so far in the Semantic Webcommunity that can motivate archaeologists to adopt theirtechnologies to manage and analysis data.

Interesting results have been obtained in the domain ofcultural heritage and museums.



The TN-LOD cloud

Tracing Networks through Linked Open Data



Tracing Networks: Vocabularies

CIDOC-CRMIt provides definitions and a formal structure for describingthe implicit and explicit concepts and relationships used incultural heritage documentation.

An ontology of 86 classes and 137 properties for cultureand more.

International standard since 2006 - ISO 21127:2006.

The ontology has been encoded in OWL2.0, OWLDL andRDFS.

http://cidoc.ics.forth.gr/index.html




Tracing Networks: Vocabularies

CIDOC-CRMIt provides definitions and a formal structure for describingthe implicit and explicit concepts and relationships used incultural heritage documentation.

An ontology of 86 classes and 137 properties for cultureand more.

International standard since 2006 - ISO 21127:2006.

The ontology has been encoded in OWL2.0, OWLDL andRDFS.

Tracing Network vocabularies extend CIDOC-CRM




Generating linked datasets University of Southampton

From RDBMs to Ontological datasets

Generating datasets for the TN-LOD



Motivation

Conventional mapping frameworks

provide scripting languages to facilitate the mapping.

apply simplistic mapping rules.



Motivation

Realistic scenarios:

the association between columns and properties is farmore complex than a simple one-to-one correspondence.

domain specific schemas to be used for mapping havebeen extended from standard vocabularies or those usedelsewhere.

Loomweights: the ontological instances conform to adomain specific schema, e.g., CIDOC-CRM.

several ontology schemas are used and the data needs tobe suitably mapped to more than one property.



The Loomweights Dataset

The column diameter in theRDB table cannot be mappedas a datatype property.

To specify a relationshipbetween diameter and theconcept Loomweight, createintermediate instances ofCIDOC-CRM concepts.

Instances to be contextuallyrelated to each other toensure loomweights areassigned correct diametervalues.



Transformation Framework

ORM Reverse Engineering.

ECA Rule-based Transformation.

Ontology Instance Generation.


SEA: Semantic Explorer for Archaeology University of Southampton

SEA:Semantic Explorer for Archaeology

MotivationThe most time-consuming part of an archaeologicalinvestigation is the post-excavation analysis.

There is therefore a mileage in combining the task ofarchiving, querying and analysing the data within a singleframework.

Archaeological data is fragmentary. Inferencing capabilitiesof reasoners can be used to extract implicit knowledge andcontribute to their existing knowledge bases to completethe fragments.



SEA:Semantic Explorer for Archaeology

A web application.

RESTful APIs for programmatically accessing the TN-LODcloud.

Interactive and global querying of linked datasets.

Data visualisations using user defined perspectives.

Statistical analysis using bespoke criteria provided byarchaeologists at runtime.



Case study: Human representations

The scope of the project includes examining and analysinghuman representations on a range of object types and in arange of materials, such as bronze and pottery.

The project utilises details such as gestures and postures,dress and associated objects as keys to understandinghow identity and new understandings of society arecommunicated.

Raw data is collected through examining objects frompublished literature or in museum collections.



Human representations: Informal queries

Example 1:

“Find images of riders who appear on objects found in Austriawhere the altitude of the excavation site is 500 meters abovesea level. I would also like to know the statistical distribution ofthe material and the technologies used for the production ofthese objects. I would like to visualise the results as a pie chartand see the distribution of the sites where these objects werefound on Google Earth”.



Human representations: Informal queries

Example 2:

“Find all objects which have images of individuals in the orantgesture who are wearing a triangular dress, earrings and whocarry a vessel on their head, where the vessel is supported bytheir left hand. I would also like to know the statisticaldistribution of the gender of these individuals according to thecountry in which the objects were found. I would like tovisualise the results as a tree map and see the distribution ofthe sites where these objects were found on Google Map”.



SEA: Architecture



SEA: Query Component

Three parts: query builder, a SPARQL/SQWRL endpointand an inference engine.

Aggregates the input data as RDF triples.

Formalises the query in SPARQL, includes any constraints.

Queries can be specified intuitively.

Utilises the WordNet dictionary.

“Natural Language Query Summariser”.

Records user preferences: statistical analysis, visualisation



SEA: Visualiser Component

Three visualisation modules.Queries generated by the user

Convert the SPARQL triple patterns to GraphMLThe visualiser is interactive and allows a user toexpand/collapse nodes in the graph.Search for a specific node in the graph.

Query Results: linked data, markers on the GoogleEarth/Google maps.

Statistical analysis: commonly used statistical analysismodels.



Human Representation

“Find images of riders who appear on objects found in Austria wherethe altitude of the excavation site is 500 meters above sea level. Iwould also like to know the statistical distribution of the material andthe technologies used for the production of these objects. I would liketo visualise the results as a pie chart and see the distribution of thesites where these objects were found on Google Earth”.

Part 1Find images of riders who appear on objects found in Austria wherethe altitude of the excavation site is 500 meters above sea level.

Part 2I would also like to know the statistical distribution of the material andthe technologies used for the production of these objects.



Building the query using SEA


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 ?materialWHERE{

?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


Visualising the query



Visualising the query results: Google earth



Visualising the query results


Related work University of Southampton

Closely related work

D2RQ: Berlin

Virtuoso: Open Link Software

STAR: Glamorgan, English Heritage

STELLAR: Glamorgan, English Heritage

TRANSLATION: Southampton


Conclusions University of Southampton

Conclusions

Little work has been done so far in the Semantic webcommunity that can motivate archaeologists to adopt theirtechnologies to manage and analysis data.

An exploratory attempt to reconstruct the Chaîneopératoire using the principles of linked open data.

A transformation framework for migrating large volumes ofarchaeological data stored in RDBs to ontology based datasets on the Semantic Web.

SEA: A unified framework that allows archaeologists withbasic knowledge of Semantic Web technologies to“explore” their datasets through interactive querying,visualisation and analysis.


Future work University of Southampton

Future work

Implement a user-friendly graphical modeling environmentfor the language in GMF (Graphical Modeling Framework)to allow easy creation and editing of transformation rules.

Extend the query interface so that it allows archaeologiststo specify ranking heuristics for the search results.

Extend the visualisation interface by providing a facetedbrowser that allows the archaeologist to visualise queryresults along several facets.

Augment the support provided for inference making.

Keeping a close eye on the linked data cloud for anyrelevant archaeological datasets that may eventually bepublished so that we can link to it.


Acknowledgements University of Southampton

Acknowledgements

Computer ScienceProf Jose Fiadeiro

Yi Hong

ArchaeologyProf Lin Foxhall

Katharina Rebay-Salisbury


University of Southampton

Many Thanks!!!


reconstructing the chaine operatoire through semantically linked open data

Technology

chane opratoirehttp

analysis data

htmlmonika solanki

technological networks

tracing network vocabularies

future workmonika solanki

wide range of material

long time period