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STELLAR Introduction Ceri Binding, Douglas Tudhope

STELLAR is a 12-month AHRC-funded project that aims to investigate semantic technologies for integrating and cross-searching archaeological datasets and grey literature. It builds on the previous STAR project and utilizes the CIDOC CRM ontology for mapping and extracting datasets into RDF/XML. The project aims to provide freely available tools and guidelines for data providers to use in order to easily map and extract datasets to the ontology.

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STELLAR Introduction Ceri Binding, Douglas Tudhope

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  1. STELLAR Introduction Ceri Binding, Douglas Tudhope Hypermedia Research Unit, University of Glamorgan

  2. 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

  3. 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 ontology together with the CRM-EH archaeological extension of the CRM along with relevant EH thesauri and glossaries

  4. STAR Project - General Architecture Applications – Server Side, Rich Client, Browser Web Services, SQL, SPARQL RDF Based Semantic Layer (CRM / CRMEH / SKOS) Data Mapping / Normalisation Indexing Conversion STAN RRAD MoLAS LEAP RPRE EH thesauri, glossaries Grey literature

  5. 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”

  6. 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.

  7. 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

  8. 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

  9. STELLAR Applications STELLAR.Console STELLAR.Web STELLAR.Win

  10. STELLAR data conversions SQL query results SQL commands Database Delimited Data import SQL query results Data from file Internal template RDF data Data from file SQL query results User-defined template Other textual Data formats

  11. Using STELLAR templates to produce RDF // 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>" • 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, name • 1, Bergamo • 2, Milano Centrale • 3, Bologna Centrale • 4, 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.

  12. Archaeology Data Service (ADS) Linked Data

  13. 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

  14. Using the RDF data RDF data output from STELLAR RDF application / triple store SPARQL queries RDF enabled applications Linked data browsers

  15. 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

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