STELLAR Introduction 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 Ceri Binding (University of Glamorgan) 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 ontology together 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) Grey literature Grey literature EH thesauri, glossaries LEAP STAN MoLAS Data Mapping / Normalisation Conversion Indexing Web Services, SQL, SPARQL Applications – Server Side, Rich Client, Browser

Natural Language Processing (NLP)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 greylitXML 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.

STAR Demonstrator – search for a conceptual pattern Research paper reports finding a coin in hearth – exist elsewhere?

Stratigraphic query

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

STELLAR templates Finds example

STELLAR applications STELLAR.Console STELLAR.Web

STELLAR.Console  Command line application  Freely downloadable  Data import / export / conversion

STELLAR.Web  Subset of STELLAR.Console functionality  CSV2RDF  RDFStats  Using same templates  Input CSV from any external application  No registration or login

CSV2DB SQL2CSV CSV2RDF SQL2RDF TAB2DB SQL2TAB TAB2RDF TAB Database CSV RDF Data Conversions Data Template

Consistent URI Construction  Namespace prefix (user defined)   Entity type (generated by template)  EHE0007 (i.e. Context)  Value (From data, URL encoded if necessary)  1015  URI pattern: {prefix}{entity type}_{value}

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

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 dataopen source 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

Contact Information Douglas Tudhope Faculty of Advanced Technology University of Glamorgan Pontypridd CF37 1DL Wales, UK STAR Research Demonstrator STAR Internet Archaeology paper (open access) NLP work - see reports with CRM and CRM-EH composite annotations in Sample Documents STELLAR tools, templates and documentation STELLAR linked data