This presentation describes the process of data integration from multiple sources to create rich descriptions of people entities and design cross-domain access services for cultural heritage resources. This work is part of an ongoing project centered on applying LOD technologies to archival performing arts content developed by the Semantic Lab at Pratt Institute.

1. Linked Data Aggregation, Integration and Mashups
in the Performing Arts: A Project Report
M. Cristina Pattuelli & Hannah Sistrunk
NKOS Workshop at DC-2017
Washington, D.C., October 28, 2017

2. Open Data Linking
Aggregation
Integration
Mashups

3. Background
& Context

4. Relationships as a tool for aggregation
Linked data generation via text mining and
entity extraction:
1. Named-entity Recognition
2. Entity Reconciliation
3. Entity Association

5. Mona Hinton interviewed by Milt Fillius & Michael Woods, Scottsdale, Arizona,
March 4, 1995.The Fillius Jazz Archive, Hamilton College.

6. Mona Hinton interviewed by Milt Fillius & Michael Woods, Scottsdale, Arizona,
March 4, 1995.The Fillius Jazz Archive, Hamilton College.
<http://linkedjazz/resource/Mona_Hinton>

7. Mona Hinton interviewed by Milt Fillius & Michael Woods, Scottsdale, Arizona,
March 4, 1995.The Fillius Jazz Archive, Hamilton College.
http://linkedjazz/resource/Mona_Hinton
<http://dbpedia.org/resource/Ella_Fitzgerald>

8. rel:knows_of

9. Mona Hinton interviewed by Milt Fillius & Michael Woods, Scottsdale, Arizona,
March 4, 1995.The Fillius Jazz Archive, Hamilton College.
<http://linkedjazz/resource/Mona_Hinton>
http://purl.org/vocab/relationship/knowsOf>
<http://dbpedia.org/resource/Ella_Fitzgerald>

11. LJ TRANSCRIPT ANALYZER

12. Network Graph
TYPED RELATIONSHIPS
DRIVING THE NETWORKS
<PersonA> <rel:knowsOf> <PersonB>

13. Entity Reconciliation
In order to link data effectively and correctly,
data needs to be unambiguously identified.

14. Identity Management
owl:sameAs

15. <http://data.carnegiehall.org/names/47408>
<http://xmlns.com/foaf/0.1/name>
"Ella Fitzgerald"
<http://dbpedia.org/resource/Ella_Fitzgerald>
<http://dbpedia.org/ontology/name>
"Ella Fitzgerald"
<http://linkedjazz.org/resource/Ella_Fitzgerald>
<http://xmlns.com/foaf/0.1/name>
"Ella Fitzgerald"@en
<https://musicbrainz.org/artist/54799c0e-eb45-
4eea-996d-c4d71a63c499>
<http://xmlns.com/foaf/0.1/name>
“Ella Fitzgerald”
<https://viaf.org/viaf/6148211>
< http://schema.org/familyName >
“Fitzgerald”
<https://www.wikidata.org/wiki/Q1768>
<https://www.w3.org/2000/01/
rdf-schema#label>
"Ella Fitzgerald"
sameAs
Co-referencing

16. Integration through interlinking

17. Enrich your data by consuming data from
other LOD-enabled datasets.
More complex queries
Trigger new questions
Ignite discovery
Benefits

18. Carnegie Hall Performance History Data

23. JAZZ TRANSCRIPT DATA
INTERVIEW
PASSAGE
PASSAGE
PASSAGE
INTERVIEWEE
MUSICIAN
MENTIONED
MUSICIAN
MENTIONED
PERFORMANCE
HISTORY DATA
EVENT
CONDUCTOR
PERFORMER
PERFORMER
DATE
VENUE
WORK
sameAs

26. Static view of the Carnegie Hall and Linked Jazz relationship visualization

27. The results were made available for exploration through an interactive Gephi visualization.

29. JAZZ TRANSCRIPT DATA
INTERVIEW
PASSAGE
PASSAGE
PASSAGE
INTERVIEWEE
MUSICIAN
MENTIONED
MUSICIAN
MENTIONED
PERFORMANCE
HISTORY DATA
EVENT
CONDUCTOR
PERFORMER
PERFORMER
DATE
VENUE
WORK
sameAs
NAME
BIRTH
DATE
DEATH
DATE
BIRTH
PLACE
ROLE
ENSEMBL
E
INSTRUM
ENT
Mashup

30. MUSICIAN
MENTIONED
PERFORMER
NAME
BIRTH
DATE
DEATH
DATE
BIRTH
PLACE
ROLE
ENSEMBL
E
INSTRUM
ENT
sameAs
NAME
DEPICTIO
N
SAME AS
KNOWS
OF
COLLABORA
TED WITH
MENTOR
OF
FRIEND OF
PLAYED
TOGETHER
IN BAND
TOGETHER
BAND
MEMBER
ACQUAINTA
NCE OF
TOURED
WITH
sameAs

31. JAZZ TRANSCRIPT DATA
INTERVIEW
PASSAGE
PASSAGE
PASSAGE
INTERVIEWEE
MUSICIAN
MENTIONED
MUSICIAN
MENTIONED
PERFORMANCE
HISTORY DATA
EVENT
CONDUCTOR
PERFORMER
PERFORMER
DATE
VENUE
WORK
sameAs
NAME
BIRTH
DATE
DEATH
DATE
BIRTH
PLACE
ROLE
ENSEMBL
E
INSTRUM
ENT
Mashup

32. Interview with Mary Lou Williams
conducted by John S.Wilson
June 26, 1973
Smithsonian Institution
Jazz Oral History Project
Mary LouWilliams

34. Little Bursts in the Wild
“The best use of your data will be thought by
somebody else in likely unanticipated ways.”

35. The JAZZCATS (Jazz Collection of
Aggregated Triples) Project
“A collection of aggregated RDF triples tracing
performance history through musicological
data…bridge previously unconnected but
complementary information about jazz music.”
Daniel Bangert,Alfie Abdul-Rahman, and Terhi Nurmikko-Fuller
(UNSW, Oxford University,Australia National University)
http://jazzcats.oerc.ox.ac.uk/

36. Discography of over 200 recordings

37. Solos within performances, including pitch, key, and chord changes

40. JazzTube
Annotations of jazz solos from the Weimar
Jazz Database (WJD) + discographies
A joint project between the Hochschule für Musik Franz Liszt Weimar (University of Music
Franz Liszt Weimar) and the International Audio Laboratories Erlangen.
http://mir.audiolabs.uni-erlangen.de/jazztube/about

41. http://mir.audiolabs.uni-erlangen.de/jazztube/soloists/

42. Challenges
Data Cooking: 80% work involved is
in data preparation and transformation
– web scraping, wrangling, cleaning, assuring quality
and consistency

43. Challenges
Skill-sets needed for information professionals
Not established methodologies
Organizational limitations (e.g., lack of
resources) and unequal opportunities for
communities to participate
Need to low-barrier tools for linked data
engineering tasks

44. Thank you! Questions?
Linked Jazz
https://linkedjazz.org
@semlabteam
Thanks to: Karen Hwang; Matt
Miller; Rachel Egan; Bill Levay
Cristina Pattuelli
mpattuel@pratt.edu
@cristinapattuel
Hannah Sistrunk
hsistrunk@rockarch.org
@HaSistrunk
SEMANTIC LAB @PRATTCREDITS: Icons from the Noun Project collection
Photos from Wikimedia Commons