1.
Frédéric Julien
Canadian Arts Presenting
Association (CAPACOA)
Annelise Larson
Veria
Assembling
A Linked
Ecosystem
for the
Performing
Arts
Photo: J’aime Hydro by Christine Beaulieu. Co-produced by Porte Parole and Champ gauche. Photo credit: Pierre Antoine Lafon Simard.
Unless otherwise noted, the content of these slides is provided under the CC BY 4.0 license.
Toronto
Halifax
Vancouver
October 21, 24
November 18
2019

2.
How has the digital
revolution transformed
the world performing arts
organizations operate in?
How should performing arts
organizations adapt to
this shift?
How has the digital
revolution transformed the
world
performing arts
organizations operate in?
How should performing arts
organizations adapt to this

3.
Business models in the
digital economy
A few strategic observations

4.
Lessons from the digital economy
Successful business models in
the digital world:
• Tied to distribution
• Rely on scale
• Create value with users’
data
• Highly personalized,
customer-focused
recommendations
The performing arts sector:
• Is focused on
creation/production
• Does not have a scalable
product
• Does not have much of a
data culture
• Recommendations focused
on the arts organization

5.
Performing arts in the digital economy
The performing arts sector:
• Must remain focused on its core business:
creation/production
• Can achieve scale through digital collaboration
• Needs to develop a brand new data culture
• Must adopt a co-opetition mindset to recommendation

6.
The Web
has been changing
Initially driven by a collaborative vision
Now driven mainly by commercial interests

7.
The Web of documents
A “vague but exciting” idea… Documents coded with
HyperText Markup Language
(HTML)
+
Uniform Resource Locator (URL)
+
HyperText Transfer Protocole
(HTTP)
=
Photo: The computer that Tim Berners-Lee used to invent the World Wide Web, in 1989.
By Robert Scoble from Half Moon Bay, USA, CC BY 2.0.

8.
The Web of data
• Tim Berners-Lee also envisioned
that the Web of documents would
evolve into a Web of data:
• Same HTTP protocol
• Uniform Resource Identifier
(URI) assigned to:
• things/objects
• and their relations
Photo: Tim Berners-Lee in 2009
By Levi Clarke - Own work, CC BY-SA 4.0

9.
The Web of data: from vision to reality
1994
URI
working
group
2001
Berners-
Lee
envisions
“data Web”
1995 2000 2005 2010
2004
Resource
Description
Framework
(RDF)
2006
Five-star
linked
open
data
2007
Freebase
DBpedia
2008
SPARQL
query
language
2010
JSON-
LD
encoding
format

10.
The Web of linked open data
The Web of data / linked open data
• provides a common framework
• that allows data to be
shared and reused
• across application, enterprise, and
community boundaries.
Source: W3C, Semantic Web Activity, 2001.

11.
Who has data to expose as linked
open data?
• Who in the room publishes information
about live performances on a website?
• How do you do it?
• Let me guess: someone copies and pastes
information from some text document into
a web page.
• What if this data only needed to
populated once? And could be reused in
several listings?

12.
Cross-domain
• Freebase
• DBpedia
Geography
• Names of places
Life Sciences
• Diseases, drugs, genes
Music
• Musicbrainz
95 datasets
The Linked Open
Data Cloud in
2009

13.
The Linked Open Data Cloud in 2014
570 datasets

14.
Linked open data
in 2019
1240 datasets
• Twice as much as in 2014!
The performing arts
aren’t there yet.

15.
And then…
Transnational tech giants also saw the potential of
linked open data.
• schema.org structured data vocabulary created in 2011
by Bing, Google, Yahoo!, and Yandex
• Google…
• Acquired Freebase
• Integrated Freebase in the Google proprietary knowledge graph;
• Shut down Freebase 2014 and moved the data into Wikidata.

16.
From search engines to
recommendation systems

17.
Welcome to the recommendation era
• Today, the majority of search queries are made on a
small screen (or without any screen).
• Search engines have therefore gradually shifted
from delivering lists of search results
to delivering recommendations.

18.
Welcome to the recommendation era
• In order to make recommendations,
search/recommendation technologies need:
Data
Data on
the offer
User data
Re
commend
ation

19.
Recommendation =
matching offers with behaviours and context
Recommendation services
take into account:
• Your online behaviour
history;
• The online behaviour of
other consumers;
• Similarities between you and
other consumers (“people
who liked this also liked
this”);
• Context (time and location).OFFER

20.
These aren’t challenges
you can tackle on your own

21.
Your real competition comes from outside
of the performing arts
• A performing arts venue may present up to 8
performances of the same show per week
• A movie theatre screens 50+ films in various
genres per week
• Netflix allows you to watch any film you want,
whenever you want, and on whatever device you
want

22.
We’re no match. And we’re behind.
Movie industry
• Commercial movies have a
unique persistent identifier
in one of several open-data
knowledge bases:
• International Standard
Audiovisual Number (ISAN)
• Entertainment Identifier
Registry (EIDR)
• Internet Movie Database
(IMDb)
Performing arts
• There are no unique
identifiers for performing
arts productions.
• There is no open
knowledge base for the
performing arts.
• There is no standardized
data model to describe
the performing arts

23.
Try for yourself
Search: “Movies near me” Search: “Shows near me”

24.
To stand a chance, we must stand
together
Anytime
Anywhere
Any device
Anytime
Anywhere
Any venue
PERFORMING
ARTS

25.
In summary
• The Web has changed into a Web of data
• Consumption is now mediated by
data-hungry algorithms
• The performing arts are behind
• We need to catch up together

26.
Solutions?
Research converges in one
direction : the performing
arts sector needs…
1. A shared data
standard;
2. Good quality,
interoperable data
published as
linked open data

27.
Questions so far?

28.
The Linked Digital Future Initiative
A multi-prong approach:
• Action-Research
• Deliver a shared data model
• Prototyping
• Translate performing arts
information into
linked open data
• Digital literacy
• Help arts organizations
adapt to the digital shift &
develop new digital
collaboration skills
Interoperability
Discoverability
Digital
transformation
Collaboration
across the value chain

29.
A
Value Chain
Approach
The Performing Arts
System (adapted from
Bonet & Schargorodsky
2018)

30.
An interoperable data model
The semantic layer

31.
What kind of data are we talking
about?
Everyone is familiar with:
• Financial data
• Ticketing and donor data
• Volunteer data
• Marketing data
• Performance measurement data
In order to have meaning and value, this data needs to
be connected to another type of data:
• Industry data

32.
LDFI Conceptual Model / Sample Data
Photo: J’aime Hydro by Christine Beaulieu. Co-produced by Porte Parole and Champ gauche. Photo credit: Pierre Antoine Lafond Simard.
Named entity
Class of similar entities

33.
LDFI Conceptual Model / Sample Data
Subject Predicate Object
J’aime Hydo Is an
instance of
Performing
arts
production
The same information can be expressed as a
Resource Description Framework (RDF) triple

34.
LDFI Conceptual Model / Sample Data

35.
LDFI Conceptual Model / Sample Data

36.
LDFI Conceptual Model / Sample Data

37.
LDFI Conceptual Model / Sample Data

38.
A distributed database
The data layer

39.
A distributed database
• Imagine many databases,
in different locations,
connected to one another…
• This is made possible with:
• Shared performing arts
ontology;
• Graph databases; and
• Data exposed as linked
open data

40.
Databases
• ISNI
• VIAF
• MusicBrainz
• Discogs
• IMDb
• Songkick
• Wikidata
Relevant Base Registers / Authority Files
Named Entities
• Works (literary, musical, choreographic)
• Editions/Translations of Works
• Character Roles
• Performing Arts Buildings
• Organizations (presenting organizations, musical
ensembles, theatre troupes, dance troupes)
• Humans (writers, composers, performing arts
professionals)
Base registers and authority files
play a key role in
interlinking datasets
from various sources.
Some statistics (Wikidata, April 2019)
• 420’000 musical works
• 21’000 plays
• 820 choreographic works
• 11’000 character roles
• 20’000 performing arts buildings
• 260’000 musicians
• 250’000 actors/actresses
• 87’000 musical ensembles
• 5’000 theatre troupes
• 340 dance troupes
and steadily growing...
Databases
• ISNI
• VIAF
• MusicBrainz
• Discogs
• IMDb
• Songkick
• Wikidata

41.
A linked ecosystem for the
performing arts

42.
The Vision: Many Stakeholders – One Knowledge Base
Performing Arts Value Chain International Knowledge
Base for the Performing Arts
One distributed
knowledge base
Many
Stakeholders
Many
applications

43.
Questions?

44.
Next steps
For the Linked Digital Future Initiative
For stakeholders of the performing arts sector

45.
Research report recommendations
1. Populate a Canadian performing arts
knowledge graph.
2. Populate data in Wikidata.
3. Develop a data governance
framework.
4. Foster the adoption of linked open
data in existing and emerging
services.
5. Develop and describe novel business
models that leverage linked open data.

46.
Next steps
• Data model: publish version 1.0
and continue development
• Knowledge graph: populate data
from many sources with
prototyping partners
• Digital literacy + communication:
raise awareness, provide
guidance and foster digital
collaboration.
• Governance: identify and address
critical questions.
• Global: pursue international
coordination of the data model.

47.
Populating a Canadian knowledge graph
for the performing arts

48.
You need guidance?
Digital Navigation
Program
• A single-window access to
one-on-one digital literacy
and digital transformation
services for performing
arts and service
organizations.
Find out more

49.
Learn more about a Linked Digital
Future
linkeddigitalfuture.ca
• Ask for guidance from a
Digital Navigator
• Participate in a Digital
Discoverability cohort
• Learn more about linked
open data

50.
Thank you for being part of the digital
shift
Akoulina Connell Bridget MacIntosh
Annelise Larson
Jai Djwa
Frédéric Julien
Rebecca Ford
Joyce Wan
Find out more about the entire team at
linkeddigitalfuture.ca

51.
Acknowledgements
Advisory Committee
• Jean-Robert Bisaillon, President and
Founder, iconoclaste musique inc. -
metaD - TGiT
• Clément Laberge, independent
consultant, education, culture and
technology
• Margaret Lam, Founder, BeMused
Network
• Tammy Lee, CEO, Culture Creates
• Mariel Marshall, Co-Founder,
StagePage
• Marie-Pier Pilote, Responsable des
projets et du développement
numérique, RIDEAU
Researcher and key contributors
• Beat Estermann, Bern University of
Applied Sciences
• Gregory Saumier- Finch, CTO,
Culture Creates
• Adrian Gschwend, Zazuko GmbH
• And many, many more contributors
to specific sections of the report.
Funding partners

52.
With thanks to the Linked Digital Future
collaborators and funding partners

53.
Key concepts

54.
Interoperability Interoperability is the ability of a
system or an application to work
(connect, exchange information,
make use of information) with
other systems or applications, at
the current time and in the future.
• For example, systems that use the same
Linked Open Data standards are
interoperable semantically and
technically: they can understand one
another’s information, and they can
exchange it without even needing to
connect through an intermediary such
as an application programming interface
(API).

55.
Discoverability Discoverability is the ability of
information:
• to be easily found when specifically
searched for;
• to be recommended when search
for;
• to be readily available when not
specifically searched for;
• and to be explored in more details.
Currently, much information about the
performing arts in Canada is not even
findable by traditional search with a
search engine.

56.
Value chain A value chain or production chain
(which is referred to as 'creative chain'
in the Conceptual Framework for
Culture Statistics) has been described
as a sequence of activities during
which value is added to a new product
or service as it makes its way from
invention to final distribution. The
stages of the creative value chain are:
creation, production, dissemination
and use.
Linked Open Data can be created at
each stage of the value chain and flow
all the way through to end users.

57.
Knowledge Graph Even experts disagree as to what a
“knowledge graph” actually is. In
simple terms, one could say that a
knowledge graph is the
combination of two things:
1. A data model (a conceptual
model for representing
information as data, with formal
ontologies providing a set of
rules about how knowledge
must be organized within a
given knowledge domain); and,
2. The actual data, stored in a
graph database.
Read more