Taking Action: Linked Data for Digital Library Managers Silvia Southwick and Cory Lampert UNLV Digital Collections American Library Association Annual.

Taking Action: Linked Data for Digital Library Managers

Silvia Southwick and Cory LampertUNLV Digital Collections

American Library Association Annual MeetingJune 28, 2014Las Vegas, NV

Agenda

• Motivation • Environment• UNLV Linked Data project• Technologies used for transforming metadata into linked

data• Visualizations of linked data (demos)• Next steps and Q & A

Linked Data Overview

• My collections are already visible through Google; so who cares• This is a topic for catalogers• It’s too technical / complicated / boring

Actually ... • Linked data is the future of the Web• Data will no longer be in trapped in silos imposed by systems, collections, or

records • Exposed open data presents new opportunities for users

What is Linked Data?

• Linked Data refers to a set of best practices for publishing and interlinking data on the Web

• Data needs to be machine-readable

• Linked data (Web of Data) is an expansion of the Web we know (Web of documents)

Current Practice

• Data (or metadata) encapsulated in records• Records contained in collections• Very few links are created within and/or across collections• Links have to be manually created• Existing links do not specify the nature of the relationships

among recordsThis structure hides potential links within and across collections

What we can do with linked data

• Free data from silos• Expose relationships• Powerful, seamless, interlinking of our data• Users interact or query data in new ways• Search results would be more precise• Data can be easily repurposed

Why?Our data needs an upgrade.

http://5stardata.info/

The Linked Open Data Cloud

Making the Case for Linked Data

Problem: – Rich metadata is being lost when adopting a standard that is

designed for interoperability (Dublin Core)– Rationale for adopting linked data is being disseminated, but there

is very little practical implementation to serve as reference; no “recipe” or uniform solution

– Evolving beyond records takes resources and requires embracing an exciting but uncertain future

Example of a metadata record

How can we create linked data?

• Our metadata records are deconstructed in triples (statements) that are machine-readable

• Triples are expressed as: Subject – Predicate - Object For example: This book – has creator – Tom Heath

This book – has title – Linked Data: Evolving the…” • Subjects, predicates and most objects should have unique identifiers

(URIs) creating data that can be used in Web architecture (HTTP)• These statements are expressed using the Resource Description

Framework (RDF)• Linked data can be queried using SPARQL

Expressing metadata as triples

• <this thing> <has creator> <Las Vegas News Bureau>• < this thing > <has genre> <Photographic print>• < this thing> <depicts> <Frank Sinatra>• < this thing> <depicts> <Jack Entratter>-------------------------------------------------------------------• <Frank Sinatra> <has profession> <entertainer>• <Jack Entratter> <has profession> <theatrical producer>

Graphic Representation

Triples and RDF

– Once we have triples we need to:

– Assign URIs to each subject

– URIs definitely are used for subjects, and might also represent objects.

URIs are essential for constructing RDF statementsThese steps take the human readable graph and make it machine readable!

Examples of records

Showgirls Menus

Dreaming theSkyline

title

How can I transform textual triples into machine-readable?

• We need vocabularies to express our triples• Even better – a data model with these vocabularies• Europeana Data Model gives us a framework to help

organize, structure, and define which predicates we are going to use

• Adopting an existing model is preferable to creating your own (interoperability)

title

Triples with URIs & EDM model predicates

(Local URI)

Machine-readable triple@prefix dc: <http://purl.org/dc/elements/1.1/> . @prefix edm: <http://www.europeana.eu/schemas/edm/> . @prefix foaf: <http://xmlns.com/foaf/0.1/> .

<http://digloc7.library.unlv.edu:8890/ProvidedCHO/sho000071> dc:creator http://digcol7.library.unlv.edu:8890/Agent/Las-Vegas-News-Bureau .

<http://digloc7.library.unlv.edu:8890/ProvidedCHO/sho000071> foaf:depicts <http://id.loc.gov/authorities/names/n50026395> .

<http://digloc7.library.unlv.edu:8890/ProvidedCHO/sho000071> edm:hasType http://id.loc.gov/vocabulary/graphicMaterials/tgm007779 .

“I’m a digital collections manager”…

• What is known? – lots of THEORY and lots of TECHNICAL information

• What is happening? – a move toward PRACTICE and APPLICATION in libraries by non-programmers

• Is there a “recipe” yet? - No. But, our staff CAN do significant work to prepare for linked data and to understand linked data principles, even if it isn’t realistic to run a parallel process.

UNLV Linked Data ProjectGoals: • Study the feasibility of developing a common process that

would allow the conversion of our collection records into linked data preserving their original expressivity and richness

• Publish data from our collections in the Linked Data Cloud to improve discoverability and connections with other related data sets on the Web

Actions Technologies

Clean dataExport data

Import dataPublish

Open Refine

Mulgara /Virtuoso

CONTENTdm

Import dataPrepare dataGenerate triplesExport RDF

Phase 1

• Clean data

• Export data

Clean / Export Data

Technology: CONTENTdm• Increase consistency across collections: – metadata element labels– use of well-known CVs– share local CVs– etc.

• Export data as spreadsheet

Phase 2

• Import to OpenRefine• Prepare (Reconcile)• Generate triples• Export RDF files

OpenRefine

• Open source

• It is a server – can communicate with other datasets via http

• Open Refine and its RDF extension should be installed

Screenshots to show some of the functions we have used

Import

Facets

Split multi-value cells

Facet view forGraphic Elementsafter splitting

Reconciliation

Specifying Reconciliation service

Activating Reconciliation

Creating the Mapping (Skeleton)

Exporting RDF files

Actions Technologies

Prepare dataExport data

Import dataPublishQuery

Open Refine

Mulgara /Virtuoso

CONTENTdm

Import dataPrepare dataGenerate triplesExport RDF

Phase 3

• Import data• Publish• Query

Mulgara Triple Store: Import

Simple SPARQL Query

Select *

Where {?s ?p ?o} limit 100

Visualization Open Source Tools

• OpenLink Virtuoso Pivot Viewer

• RelFinder

UNLV Linked Data Blog with videos: http://www.library.unlv.edu/linked-data/2014/04/selected-presentations-project.html

• Good for displaying images

• Selection of images through SPARQL Queries

• Allows refinements using facets

• Allows creating dynamic “collections”

OpenLink Pivot Viewer

SPARQLQuery

Costume DesignDrawings

Showgirls

• Video clip: http://youtu.be/-83FTKEkYZ0

Example of Pivot Viewer

RelFinder

• Good to show relationships:– Among people – Among “things”

• Show type of relationships

Demos:– African American Experience in Las Vegas (Oral History): http://

youtu.be/wKCEl3KXdGk – Cross collections people relationship: http://youtu.be/co3nlbMkoWE

• Video clip

Examples of Relfinder

Next steps for the UNLV project

• Transform all digital collections into linked data (parallel structure)

• Publish our collections metadata as Linked Open Data• Increase linkage with other datasets• Design and assess user friendly interfaces to access and

display our data and related data from other datasets• Produce a cost benefit analysis to inform future plans for the

development of digital collections

Our Experience

• Project led, implemented and managed by two busy faculty librarians

• No model to follow; our model was experimentation and research

• With interest and motivation, Linked Open Data is a feasible goal

Thank You!

Questions?

Cory LampertHead, Digital [email protected]

Silvia SouthwickDigital Collections Metadata [email protected]