KOS-based tools for archaeological dataset interoperability: NKOS Workshop, ECDL 2010 C. Binding, K. May 1, D. Tudhope, A. Vlachidis Hypermedia Research.

Slides:



Advertisements
Similar presentations
Dr. Leo Obrst MITRE Information Semantics Information Discovery & Understanding Command & Control Center February 6, 2014February 6, 2014February 6, 2014.
Advertisements

Alexandria Digital Library Project Integration of Knowledge Organization Systems into Digital Library Architectures Linda Hill, Olha Buchel, Greg Janée.
Controlled Vocabularies in TELPlus Antoine ISAAC Vrije Universiteit Amsterdam EDLProject Workshop November 2007.
A Prototype Implementation of a Framework for Organising Virtual Exhibitions over the Web Ali Elbekai, Nick Rossiter School of Computing, Engineering and.
Archaeology and Terminology Ceri Binding Hypermedia Research Unit, University of Glamorgan, Wales, UK
Interoperability Aspects in Europeana Antoine Isaac Workshop on Research Metadata in Context 7./8. September 2010, Nijmegen.
Ceri Binding Hypermedia Research Unit, University of Glamorgan, Wales
STELLAR Introduction Ceri Binding, Douglas Tudhope Hypermedia Research Unit, University of Glamorgan.
Semantic Annotations in the Archaeological Domain Andreas Vlachidis, Ceri Binding, Keith May, Douglas TudhopeSTAR STAR Semantic Technologies for Archaeological.
Thesauri, Terminologies and the Semantic Web
STELLAR Introduction Douglas Tudhope Hypermedia Research Unit, University of Glamorgan.
SKOS and Other W3C Vocabulary Related Activities Gail Hodge Information International Assoc. NKOS Workshop Denver, CO June 10, 2005.
Data Sources & Using VIVO Data Visualizing Scholarship VIVO provides network analysis and visualization tools to maximize the benefits afforded by the.
Semantic Mediation & OWS 8 Glenn Guempel
Cloud based linked data platform for Structural Engineering Experiment Xiaohui Zhang
Chapter 2 Database System Concepts and Architecture
MDC Open Information Model West Virginia University CS486 Presentation Feb 18, 2000 Lijian Liu (OIM:
Networking Session: Global Information Structures for Science & Cultural Heritage - The Interoperability Challenge «INTEROPERABILITY FROM THE CULTURAL.
Linking Disparate Datasets of the Earth Sciences with the SemantEco Annotator Session: Managing Ecological Data for Effective Use and Reuse Patrice Seyed.
Database System Concepts and Architecture Lecture # 3 22 June 2012 National University of Computer and Emerging Sciences.
Interoperable Digitised Content “Discover, search, extract, link, associate, and view digitised content” Les Carr.
Database System Concepts and Architecture Lecture # 2 21 June 2012 National University of Computer and Emerging Sciences.
INF 384 C, Spring 2009 Ontologies Knowledge representation to support computer reasoning.
Lushan Han, Tim Finin, Cynthia Parr, Joel Sachs, and Anupam Joshi RDF123: from Spreadsheets to RDF.
Miscellaneous Excel Combining Excel and Access. – Importing, exporting and linking Parsing and manipulating data. 1.
A centre of expertise in digital information management The MEG Metadata Schemas Registry Pete Johnston, Research Officer (Interoperability),
Dynamic Hypermedia Generations through a Mediator using CRM and Web Service Jen-Shin Hong National ChiNan University,Taiwan
Computer Science 101 Database Concepts. Database Collection of related data Models real world “universe” Reflects changes Specific purposes and audience.
1 Issues in Reusing and Sharing the Content of Thesauri and Taxonomies in OOR Marcia Zeng NKOS (Networked Knowledge Organization Systems/Services) My participating.
Scalable Metadata Definition Frameworks Raymond Plante NCSA/NVO Toward an International Virtual Observatory How do we encourage a smooth evolution of metadata.
MD9.6 Release: Highlights Increased the character limit for all URL resources to 600 characters. Data_Center/Service_Provider Data_Set_Citation/Service_Citation.
The Agricultural Ontology Service (AOS) A Tool for Facilitating Access to Knowledge AGRIS/CARIS and Documentation Group Library and Documentation Systems.
NERC DataGrid NERC DataGrid Vocabulary Server Use Cases Vocabulary Workshop, RAL, February 25, 2009.
CLARIN work packages. Conference Place yyyy-mm-dd
, 1/21, © Library and Documentation Systems Division 21 st APAN Meeting Tokyo, January 2006 AGROVOC and AOS, Margherita Sini, FAO From.
Semantic Annotation of Grey Literature from an Archaeological Digital Library Andreas Vlachidis, Doug Tudhope Hypermedia Research Unit University of Glamorgan.
MANAGING DATA RESOURCES ~ pertemuan 7 ~ Oleh: Ir. Abdul Hayat, MTI.
Interoperability & Knowledge Sharing Advisor: Dr. Sudha Ram Dr. Jinsoo Park Kangsuk Kim (former MS Student) Yousub Hwang (Ph.D. Student)
Efficient RDF Storage and Retrieval in Jena2 Written by: Kevin Wilkinson, Craig Sayers, Harumi Kuno, Dave Reynolds Presented by: Umer Fareed 파리드.
Chapter 2 Database System Concepts and Architecture Dr. Bernard Chen Ph.D. University of Central Arkansas.
SKOS. Ontologies Metadata –Resources marked-up with descriptions of their content. No good unless everyone speaks the same language; Terminologies –Provide.
N NESSTAR: A Semantic Web Application for Statistical Data and Metadata Pasqualino “Titto” Assini Nesstar Ltd - UK.
OWL Representing Information Using the Web Ontology Language.
User Profiling using Semantic Web Group members: Ashwin Somaiah Asha Stephen Charlie Sudharshan Reddy.
ESIP Semantic Web Products and Services ‘triples’ “tutorial” aka sausage making ESIP SW Cluster, Jan ed.
Issues in Ontology-based Information integration By Zhan Cui, Dean Jones and Paul O’Brien.
A Resource Discovery Service for the Library of Texas Requirements, Architecture, and Interoperability Testing William E. Moen, Ph.D. Principal Investigator.
Personalized Recommendation of Related Content Based on Automatic Metadata Extraction Andreas Nauerz 1, Fedor Bakalov 2, Birgitta.
1 Open Ontology Repository initiative - Planning Meeting - Thu Co-conveners: PeterYim, LeoObrst & MikeDean ref.:
Object storage and object interoperability
Open Archive Forum Rachel Heery UKOLN, University of Bath UKOLN is funded by Resource: The Council for Museums, Archives.
Steven Perry Dave Vieglais. W a s a b i Web Applications for the Semantic Architecture of Biodiversity Informatics Overview WASABI is a framework for.
1 DMS-DQS-SUPSC03-PRE-12-E © DEIMOS Space S.L., 2007 A Semantic Data Grid for Satellite Mission Quality Analysis Reuben Wright Deimos Space.
2) Database System Concepts and Architecture. Slide 2- 2 Outline Data Models and Their Categories Schemas, Instances, and States Three-Schema Architecture.
STAR, STELLAR and SKOS Ceri Binding, Phil Carlisle, Keith May, Doug Tudhope, Andreas Vlachidis University of Glamorgan and English Heritage.
Building Preservation Environments with Data Grid Technology Reagan W. Moore Presenter: Praveen Namburi.
ARIADNE is funded by the European Commission's Seventh Framework Programme Interoperability Holly Wright.
OWL (Ontology Web Language and Applications) Maw-Sheng Horng Department of Mathematics and Information Education National Taipei University of Education.
TextCrowd – Collaborative semantic enrichment of text-based datasets
Cloud based linked data platform for Structural Engineering Experiment
Chapter 2 Database System Concepts and Architecture
Integrating Data for Archaeology
Workshop on distributed architecture and web servcies
MANAGING DATA RESOURCES
ESS roadmap on Linked Open Data State of play
LOD reference architecture
C. Binding, K. May1, R. Souza, D. Tudhope, A. Vlachidis
Breaking Down Barriers to Interoperability
SDMX IT Tools SDMX Registry
Presentation transcript:

KOS-based tools for archaeological dataset interoperability: NKOS Workshop, ECDL 2010 C. Binding, K. May 1, D. Tudhope, A. Vlachidis Hypermedia Research Unit, University of Glamorgan 1 English Heritage

STAR Project Semantic Technologies for Archaeological Resources –AHRC funded project –In collaboration with English Heritage –

STAR Aims and Background Investigate the potential of semantic terminology tools for widening access to digital archaeology resources, including disparate data sets and associated grey literature Open up the grey literature to scholarly research by investigating the combination of linguistic and KOS-based methods in the digital archaeology domain. Develop new methods for enhancing linkages between digital archive database resources and to associated grey literature, exploiting the potential of a high level, core ontology. Current situation one of fragmented datasets and applications, with different terminology systems Need for integrative metadata framework EH have designed an upper ontology based on CRM standard

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

CRM Event Based model - Property chains CRM event model – events not explicit in datasets OR mappings –Additional work required to satisfy logical mappings E.g. Sample taken from Context: crmeh:EHE0018.Sample [crm:E18.PhysicalStuff]  crm:P113B.was_removed_by  crmeh:EHE2006.ContextSamplingEvent [crm:E80.PartRemoval]  crm:P112F.diminished  crmeh:EHE0008.ContextStuff [crm:E18.PhysicalStuff]  crmeh:EHP3.occupied  crmeh:EHE0007.Context [crm:E53.Place]

RDF Data Extraction Tool

Resultant extracted data (RDF/XML)

STAR implementation - linking CRM instances to SKOS concepts Property: is_represented_by (represents) Domain: crm:E55.Type Range: skos:Concept Property: is_represented_by (represents) Domain: crm:E55.Type Range: skos:Concept is_represented_by (represents)

STAR – Web Services and Client Applications STAR Web Services English Heritage thesauri (SKOS) Archaeological Datasets (CRM) Windows applications Browser components Full text search Browse concept space Navigate via expansion Cross search archaeological datasets Windows applications Browser components Full text search Browse concept space Navigate via expansion Cross search archaeological datasets STAR Client Applications STAR Datasets Grey literature indexing

STAR – Web Client Components Browse available thesauri Search across multiple thesauri Display concept details Navigate via semantic expansion

Preliminary prototype application Incorporated SKOS based thesaurus query expansion in search Colour coding of results by source dataset Browse results and drill down Open links to external data if available

STAR web browser based search interface Search parameters Search results Group detailsContext details Sample detailsFind details

Initial search Search parameters

Context details

Context find details Find details

Context sample details Sample details

Group details

Grey Literature Information Extraction ( Andreas Vlachidis) Looking to extract CRM-EH period, context, find, sample entities Aim to cross search within data

Example STAR use of URIs (NLP) walls <crmeh:EXP10F.is_represented_by rdf:resource=" <crmeh:EXP10F.is_represented_by rdf:resource=" with a...

STELLAR STELLAR aims to generalise and extend the data extraction tools produced by STAR and enable third party data providers to use them.”STELLAR The extracted data will be represented in standard formats that allow the datasets to be cross searched and linked by a variety of Semantic Web tools, following a linked data approach. Objectives Develop best practice guidelines for mapping and extraction of archaeological datasets into RDF/XML representation conforming to the CIDOC CRM-EH standard ontology Develop an enhanced tool for non-specialist users to map and extract archaeological datasets into RDF/XML representation conforming to CIDOC CRM-EH Develop best practice guidelines and tools for generating corresponding Linked DataLinked Data Evaluate the mapping tool and the Linked Data provision Engage with the archaeological community to inform research and disseminate outcomes

STELLAR - Data Processing Stages Parsing source data –Excel spreadsheets –Delimited data files (CSV) Cleansing / Manipulation –Trim space –Force uppercase / lowercase –replace text –add prefix / suffix –url encoding –Semantic enrichment Mapping –Columns to template placeholders Transformation –Apply templates to tabular data Validation –Validate output for coherence

<rdf:RDF xmlns:crm=" xmlns:rdf=" xmlns:rdfs=" SF NO 427; FRENCH 23 SITECODE CONTEXTACC_NOMATEE_DATEMAX_DIAMCOMMENTSOBJECTID BA COPP130220SF NO 258; UNOFFICIAL STERLING1649 BA COPP128523SF NO 427; FRENCH1650 BA COPP128019SF NO 418; BARBAROUS PRIVATE ISSUE1651 BA COPP141527SF NO 884; TOURNAI STOCK JETTON1652 <rdf:RDF xmlns:crm=" xmlns:rdf=" xmlns:rdfs=" $OBJECT_IDENTIFIER$ $OBJECT_NOTE$ $MEASUREMENT_VALUE$ Resultant output (per row) Tabular data columns mapped to template placeholders Data template with defined placeholders $OBJECT_URI$ = “#e19.” + ACC_NO $OBJECT_IDENTIFIER$ = ACC_NO $OBJECT_NOTE$ = COMMENTS $MEASUREMENT_VALUE$ = MAX_DIAM $PLACE_URI$ = “#e53.” + SITECODE + “-” + CONTEXT STELLAR – templates and placeholders

Linked data issues in STELLAR What parts of dataset useful to map/extract for linked data purposes? What best left as native dataset? Cost/benefit ? Lack of current domain name for some organisations within project premature/temporary URI definition? Lack of resource commitment for some organisations within project Where data reside? Demands on server? Relationship between local dataset glossaries and ‘centre’ - map to unified central super glossary? - maintain local glossaries with (SKOS) mappings to centre? Resultant application? Linked data in itself not offer search capability

Mapping issues Potentially multiple mappings can exist to a broad conceptual framework (BRICKS experience) – depends on purpose for mapping and focus of concern – STELLAR design mapping patterns (ontology  datasets) Datasets encountered not have a schema, not necessarily well structured and probably need semantic cleaning ===> infer schema for tool purposes from dataset

Mapping issues ctd. Considering various cases of mapping RDB to CRM DB column to CRM entity DB column to multiple CRM entities DB column to CRM entity with intermedite (event) nodes DB columns (in same table) concatenated to single CRM entity DB complete Table maps to a single CRM entity DB entities map to CRM entity plus set of properties

CRM is complex => need for simpler views CRM useful for integration and semantic interoperabiliy Not mirror RDF structure in RDF It is not necessary to present the user with full CRM or CRM-EH views for mapping work and search (browsing) interfaces Search / browsing / mapping interfaces can present a simplified CRM view to user, while retaining the benefits of CRM standard for inter-operability

References 1.STAR Project. 2.STAR Project Publications. 3.STELLAR Project.