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Assembling a Linked Ecosystem for the Performing Arts


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Whether we like it or not, data-hungry algorithms and AI-powered recommendation engines are now mediating all performing arts engagement online. Oddly, the technologies behind these algorithms were initially not designed for commercial interests but rather for collaboration. So, shall we simply comply with Google and Alexa’s requirements for data? Or shall we rather build a shared data ecosystem that will serve both our needs and those of bots?

This presentation was developed and delivered as part of the linked digital future initiative. For more information, visit:

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Assembling a Linked Ecosystem for the Performing Arts

  1. 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. 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. 3. Business models in the digital economy A few strategic observations
  4. 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. 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. 6. The Web has been changing Initially driven by a collaborative vision Now driven mainly by commercial interests
  7. 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. 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. 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. 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. 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. 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. 13. The Linked Open Data Cloud in 2014 570 datasets
  14. 14. Linked open data in 2019 1240 datasets • Twice as much as in 2014! The performing arts aren’t there yet.
  15. 15. And then… Transnational tech giants also saw the potential of linked open data. • 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. 16. From search engines to recommendation systems
  17. 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. 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. 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. 20. These aren’t challenges you can tackle on your own
  21. 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. 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. 23. Try for yourself Search: “Movies near me” Search: “Shows near me”
  24. 24. To stand a chance, we must stand together Anytime Anywhere Any device Anytime Anywhere Any venue PERFORMING ARTS
  25. 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. 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. 27. Questions so far?
  28. 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. 29. A Value Chain Approach The Performing Arts System (adapted from Bonet & Schargorodsky 2018)
  30. 30. An interoperable data model The semantic layer
  31. 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. 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. 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. 34. LDFI Conceptual Model / Sample Data
  35. 35. LDFI Conceptual Model / Sample Data
  36. 36. LDFI Conceptual Model / Sample Data
  37. 37. LDFI Conceptual Model / Sample Data
  38. 38. A distributed database The data layer
  39. 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. 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. 41. A linked ecosystem for the performing arts
  42. 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. 43. Questions?
  44. 44. Next steps For the Linked Digital Future Initiative For stakeholders of the performing arts sector
  45. 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. 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. 47. Populating a Canadian knowledge graph for the performing arts
  48. 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. 49. Learn more about a Linked Digital Future • Ask for guidance from a Digital Navigator • Participate in a Digital Discoverability cohort • Learn more about linked open data
  50. 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
  51. 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. 52. With thanks to the Linked Digital Future collaborators and funding partners
  53. 53. Key concepts
  54. 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. 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. 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. 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