NISO Webinar: Library Linked Data:

From Vision to Reality

December 11, 2013

Speakers: Jon Voss - Strategic Partnerships Director, We Are What We Do

Matt Miller - Front End Developer, NYPL Labs at the New York Public Library

Silvia Southwick - Digital Collections Metadata Librarian, UNLV University Libraries

Cory Lampert - Head, Digital Collections , UNLV University Libraries

http://www.niso.org/news/events/2013/webinars/linked_data

Linked JazzRevealing the

relationships of the jazz community

Matt Miller@thisismmillerDecember 2013

Project Overview

• Investigating the application of Linked Open Data to enhance the discovery and visibility of digital cultural heritage materials.

• Build new methods of connecting cultural data.• Uncover meaningful connections between

documents and data related to the personal and professional lives of musicians who often practice in rich and diverse social networks.

Professor Cristina Pattuelli at the Pratt Institute School of Library Information Science is the director of the project which began in 2011.

Linked Data Now!

Why?• Bootstrap your project with existing data.• Highlights knowledge you have created and

knowledge that is missing. • Facilitates sharing, but also growing your own

project.

Bootstrapping – Identifying

Research QuestionHow can we discover and analyze the rich and diverse network of

relationships between jazz musicians?

Primary SourcesOral history interview transcripts

of jazz musicians.

Bootstrapping – Identifying

Research QuestionHow can we discover and analyze the rich and diverse network of

relationships between jazz musicians?

Primary SourcesOral history interview transcripts

of jazz musicians.

We need to know the names (and variants) of jazz

musicians in a structured controlled vocabulary.

Bootstrapping – Identifying

Charlie Parker

Many different LOD datasets contain this information. We need to access, query and link it

for only jazz related individuals.

Bootstrapping – Querying

Bootstrapping – Querying

• Processing the DBpedia dataset resulted in around 9,000 URIs.– DBpedia is fluid! After each release (currently 3.9) we

reprocess the files resulting in the addition of 500-700 URIs.

• We now have a name directory, but we want additional forms of personal names. To accomplish this we try mapping to Library of Congress.

• Matching DBpedia and LC URIs is not automatic.

Bootstrapping – Mapping• We matched identities based on:

• Name• Life Dates• White listed words found in sources

(http://www.loc.gov/mads/rdf/v1#Source)

• Reconciling authorities is difficult!• Use others work: http://viaf.org/viaf/data/

• But don’t discount your own processes.• Using our relatively simple process we

were able to match about 1500 more URIs than VIAF.org.

• This is due to a smaller domain (jazz).

Our name directory creation and authority matching is documented:

https://github.com/thisismattmiller/linked-jazz-name-directory

Bootstrapping – Curating

http://linkedjazz.org/public_demo_mapping/

Bootstrapping – Review

• Start small, think big.– Specific subject domain.– Large infrastructure not required (triple stores, etc.)

• Can get started with extract files and python scripting.

• Reuse as much as possible, but try new processes leveraging domain specificity.

• Always be curating, use tools to facilitate process but a human hand is often required.

Applying the Data

• Use the name directory to locate individuals in the interview transcript.

• This project phase involves 50 transcripts.• Because the names are tied to URIs we can

infer a relationship triple between two individuals.

<foaf:Person> <rel:knowsOf> <foaf:Person>

Applying the Data

Transcript Analyzer

Transcript Analyzer

• An interface to curate the transcripts and verify detected names.

• Implements off the shelf NLP (NLTK) to attempt to locate additional names not in our directory as well as corporate names and locations.

• Global rule system, as we process more transcripts the system is being trained.

• Using URIs to represent entities we can quickly see where we are discovering new material.– 50 Transcripts

• 1800 person entities tagged.• 250 names tagged without authoritative URI.

– Knowledge Creation

New Dataset

• We have created a new LOD dataset now of jazz musician’s relationships.

• Our next steps are:– Visualize.– Further qualify the rel:knowsOf relationships.– Provide access to the data created.

Visualize

http://linkedjazz.org/network/

Qualify Relationships – 52nd St.

• Recruit jazz experts and enthusiasts to help categorize relationships based on transcript text.

• We use existing vocabularies to build the data set: Foaf, Relationship Vocabulary, Music Ontology

• The interface is critical for crowdsourcing tools, we work with user experience experts and conduct user studies to refine our public facing tools

Qualify Relationships – 52nd St.

http://linkedjazz.org/52ndStreet/

Provide Access

• We provide a SPARQL endpoint.• But also a traditional API:

– http://linkedjazz.org/api/– Can return:

• JSON• N-Triples • Gephi graph files (GXEF)

Learn and Grow as a Team

• Experience through doing.

• Empower graduate students with skills and practical experience working with a LOD project.

• Use the project as a vehicle to make intra- and inter-intuitional collaborations.

Linked Jazz Team July 2013

Next Steps

• Refactor our prototype tools into sustainable open source projects.

• Redesign 52nd St. based on user study groups.• Work on emerging collaborations with Jazz Centers.

http://www.linkedjazz.org

Thanks!

Linked, Exposed Data: UNLV Linked Data Project

NISO Webinar: Library Linked Data: From Vision to RealityDecember 11, 2013

Silvia B. SouthwickDigital Collections Metadata LibrarianUNLV Libraries

Cory K. LampertHead, Digital CollectionsUNLV Libraries

Agenda

• Motivation • Environment• UNLV Linked Data project• Technologies• Transforming metadata into linked data• Next steps

How it Started

• Conferences and “buzz”• Curiousity and professional development• Exploration and pilot project• Compelling results; sharing impact of what

we’ve learned• Assessment • Much more to do...

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

Making the Case for Linked Data in Academic Library Digital Collections

– Problem: Rich metadata is being lost in dumbed down DC records

– Issue: Investment and resource allocation (Item-level philosophy)

– Goal: Increased: exposure, collaboration, and openness

– Outcome: Increased discovery and user-focus

Gaining Buy In

Administration• Innovative project, high impact• Pilot, experiment, learn by doing, share results Staff• We already have the metadata; We need to

transform them into triples• Managing change

Graphical Representation: One Record

Examples of records

Showgirls Menus

Dreaming theSkyline

titleDecember 12, 1915

Implications (Internal)

• Cross-unit collaboration is necessary• Staff expertise will evolve• Staff roles will change to accommodate new /

parallel workflow• Data clean-up will be an investment• Management of data becomes critical• Discovery issues = user interfaces still need

development

Implications (External)

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

• Sharing data in new ways with new partners may raise new issues

• Need to engage with linked data community for technologies, tools, best practices, and to demand library vendor support for LOD.

UNLV Linked Data Project

Goals: • 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

PROJECT IMPLEMENTATION

Actions Technologies

Prepare dataExport data

Import dataPublish

Open Refine

Mulgara /Virtuoso

CONTENTdm

Import dataClean dataReconcileGenerate triplesExport RDF

Prepare / Export Data

Technology: CONTENTdm

• Increase consistency across collections: – metadata element labels– use of CV, share local CVs– etc.

• Export data as spreadsheet

Create mapping between metadata elements and EDM model predicates

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

OpenRefine first screen

Facets

Split multi-value cells

Facet view forGraphic Elementsafter splitting

Reconciliation

Specifying Reconciliation service

Activating Reconciliation

Creating a Skeleton

Exporting RDF files

Actions Technologies

Prepare dataExport data

Import dataPublishQuery

Open Refine

Mulgara /Virtuoso

CONTENTdm

Import dataClean dataReconcileGenerate triplesExport RDF

Mulgara Triple Store: Import

A simple SPARQL query

Select *

where

{ ?s ?p ?o} limit 100

SPARQL: Querying Data

• Using Virtuoso PivotViewer

Query

Costume DesignDrawings

Showgirls

Next steps for the UNLV project

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

• Increase linkage with other datasets• Design interfaces to access and display our data

and related data from other datasets• Evaluate alternative interfaces from user’s

perspective• Produce a cost benefit analysis to inform future

plans for the development of digital collections

Thank You!

Questions?

NISO Webinar • December 11, 2013

Questions?All questions will be posted with presenter answers on the NISO website following the webinar:

http://www.niso.org/news/events/2013/webinars/linked_data

NISO Webinar: Library Linked Data: From Vision to Reality

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