STELLAR IntroductionCeri Binding, Douglas Tudhope

Hypermedia Research Unit, University of Glamorgan

STELLAR

12 month AHRC funded project Hypermedia Research Unit, University of Glamorgan Archaeology Data Service, University of York– English Heritage Centre for Archaeology, Portsmouth

Builds on previous 3 year AHRC funded STAR Project

Acknowledgments

Andreas Vlachidis (University of Glamorgan)

Keith May, English Heritage (EH)

Stuart Jeffrey, Julian Richards, Michael Charno, Tim Evans, Holly Wright

Archaeology Data Service (ADS)

Archaeology Department, University of York

STAR – Aims and background

• Investigate semantic technologies for integrating and cross searching datasets and associated grey literature

• Current situation - fragmented datasets with different terminology

• Lack of semantic interoperability and cross search

• Need for integrative metadata framework CIDOC CRM (ISO standard) as high level, core ontologytogether with the CRM-EH archaeological extension of the CRM

along with relevant EH thesauri and glossaries

STAR Project - General Architecture

RRAD RPRE

RDF Based Semantic Layer (CRM / CRMEH / SKOS)

Greyliterature

EH thesauri,

glossaries

LEAPSTAN MoLAS

Data Mapping / NormalisationConversionIndexing

Web Services, SQL, SPARQL

Applications – Server Side, Rich Client, Browser

Natural Language Processing (NLP)of archaeological grey literature

Extract key concepts in same semantic representation as for data.

Allows unified searching of different datasets and grey literature

in terms of same underlying CRM-based conceptual structure

Output as RDF triples in Demonstrator and as XML with greylit

“ditch containing prehistoric pottery dating to the Late Bronze Age”

STAR Demonstrator – search for a conceptual pattern

An Internet Archaeology publication on one of the (Silchester Roman) datasets we used in STAR discusses the finding of a coin

within a hearth.-- does the same thing occur in any of the grey literature reports?

Requires comparison of extracted data with NLP indexing in terms of the ontology.

STELLAR aims and outcomes

• Make it easier to map and extract datasets to CIDOC CRM ontology

in a consistent manner

• Generalise the data extraction tools produced by STAR

so third party data providers can use them

• Develop methods for mapping and extraction of archaeological

datasets into RDF/XML conforming to CIDOC CRM-EH ontology

with unique global identifiers for entities and concepts (http URIs)

for publication as linked data

• Freely available tools and guidelines/tutorials

STELLAR background

• In practice mapping to CRM has tended to require specialist knowledge of the ontology

and been resource intensive

• Given the wide scope of the CRM, it is possible to make multiple valid mappings

depending on the intended purpose and focus of the mappings

• STELLAR tools convert archaeological data to CRM/RDF in a consistent manner,

without requiring detailed knowledge of the underlying ontology

• User chooses a template for a particular data pattern

and supplies the corresponding input from their database

(combination of optional elements with a mandatory ID)

• STELLAR templates for – CRM-EH archaeological extension to the CIDOC CRM– Some more general CIDOC CRM templates conforming to the CLAROS Project format– SKOSifying a glossary/thesaurus connected with the dataset – Also capability for user-defined templates

STELLAR Applications

STELLAR.Console

STELLAR.Web STELLAR.Win

import

DatabaseDatabase

Internal templateInternal template

SQL query results

SQL query results

Delimited DataDelimited Data

Data from file

SQL query results

User-defined template

User-defined template

RDF dataRDF data

Other textualData formatsOther textualData formats

Data from file

SQL commands

STELLAR data conversions

// STELLAR template to write RDF header

HEADER(options) ::= <<

<?xml version="1.0" encoding="UTF-8"?>

<rdf:RDF

xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"

xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#"

xmlns:crm="http://www.cidoc-crm.org/rdfs/cidoc-crm#">

>>

// Template writes RDF entities and properties based on each data row;

// $placeholder.value$ is replaced with the named field data at runtime

RECORD(options, data) ::= <<

<crm:E53 rdf:about="#E53_$data.id$">

<crm:P87F rdf:resource="#E44_$data.id$“/>

</crm:E53>

<crm:E44 rdf:about="#E44_$data.id$">

<rdfs:label xml:lang="it">$data.name$</rdfs:label>

<crm:P87B rdf:resource="#E53_$data.id$“/>

</rdf:Description>

>>

// STELLAR template to write RDF footer – closure of header elements

FOOTER(options) ::= "</rdf:RDF>"

Using STELLAR templates to produce RDF•Templates are just text files. May be copied, edited, exchanged, disseminated.

•XML/RDF syntax and namespace details are handled within the template.

•User input is simple tabular delimited textual data with named fields, e.g.:

id, name1, Bergamo2, Milano Centrale3, Bologna Centrale4, Prato Centrale

•Predefined patterns of entities, properties and inverse properties are created by the template, data populates placeholders at runtime.

•Output is consistent and repeatable.

Archaeology Data Service (ADS) Linked Data

Linked data publication by ADS

• Selected range of archived archaeological excavation datasets (academic and commercial sectors) converted to RDF using STELLAR tools

and ingested into a repository (triple store)

• The SPARQL endpoint allows consumption by semantic technologies

including Pubby (an open source linked data front end) used for publishing linked data

• Content negotiation presents data in formats appropriate for the requesting application

(eg RDF/XML/HTML browsers).

• Effort devoted to ensure URI construction appropriate for the domain.

For ADS archives this includes use of existing DOI identifier codes in the target URI.

For external data sets (not already archived with the ADS, eg from commercial contractors)

site naming conventions validated by the ADS adopted.

• The linked data outputs (and the frontend) are available from ADS website

http://data.archaeologydataservice.ac.uk

Using the RDF data

RDF application / triple store

RDF application / triple store

SPARQL queries RDF enabled applicationsLinked data

browsers

RDF data output from STELLAR

Contact Information

Douglas Tudhope

Faculty of Advanced Technology

University of Glamorgan

Pontypridd CF37 1DL

Wales, UK

dstudhope@glam.ac.uk

http://hypermedia.research.glam.ac.uk/kos/STAR/

http://hypermedia.research.glam.ac.uk/resources/star-demonstrator/

STAR Research Demonstrator

http://intarch.ac.uk/journal/issue30/tudhope_index.html

STAR Internet Archaeology paper (open access)

http://andronikos.kyklos.co.uk/aboutus.php

NLP work - see reports with CRM and CRM-EH composite annotations in Sample Documents

http://hypermedia.research.glam.ac.uk/kos/STELLAR/

http://hypermedia.research.glam.ac.uk/resources/STELLAR-applications/

STELLAR tools, templates and documentation

http://data.archaeologydataservice.ac.uk

STELLAR linked data