An presentation of how the Swedish speaking department of the Finnish public broadcaster Yle has used semantic metadata to tie it's services together, link content and make recommendations and prepare for personification.

1. Data first and linked data
at the Swedish speaking Yle
Mikael Hindsberg, concept developer svenska.yle.fi
@mickhinds | mikael.hindsberg@yle.fi
27.5.2015
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2. Linked data
We now link content at Svenska.yle.fi over:
• Organizational borders
• Content managment systems
• Different languages
• Different media types (text/video/audio)
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3. Linked data
WHY?
and
HOW?
… have we
done this
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"I Wonder". CC BY -
http://commons.wikimedia.org/wiki/File:I_Wonder.jpg#me
diaviewer/File:I_Wonder.jpg

4. Organization
Svenska Yle is a miniature of Yle
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• We are a small agile unit
who can pilot many things
within the company

5. Organization
One tv-channel and two radio channels
- And the web
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6. Big disruption in digitalizing media
Media convergence
The audience is changing the
way they consume media –
FAST!

7. How to handel this?
Unified platform, Drupal7, 2012
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News, current
affairs, sports,
entertainment,
lifestyle, recipes,
health, science,
debate – all in one
hierarchically flat
platform.
MUST ALSO
BE UNIFORM
AS DATA!

8. Referer trafic
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- Machine readability
- Semantic metadata
- Linked data
- Open data
- SEO
The content must be able to live independently from the
platform!

9. Content – i.e. articles – rule!
60% of the trafic starts from an article, 29% from the main
page. 11% covers everything else!

10. Mobile over 50%
Week Desktop Mobile Tablet
21/2014 57 % 27 % 16 %
21/2015 46 % 37 % 17%

11. Metadata is the key
• Content, platform and distribution are
exploding in diversity.
• We need to be able to serve the web with
our content as data.
• Semantically rich ontologies with public URI’s
• We use Finto (Finnish ontology and thesauri service) and
Freebase >> Wikidata ca. Aug. 2015
• Linked data
• Map relation’s between content to link them together 
• Data graph
• This graph gives structure, recommendations, search engine
optimization, new knowledge, global intercompability
= Data first
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12. The components
• Finnish ontology and thesauri library
(FINTO) www.finto.fi
• Frebase www.freebase.com
• Drupal module for annotation:
https://www.drupal.org/project/yild
• + journalists do the base annotation
• complemented with automatic annotation
• New module can utilize wikipedia,
geonames > almost all open metadata
repositories: YILD – Yle Integrator for
Linked Data
https://www.drupal.org/project/yild
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13. YILD
Yle Integrator for Linked Data
https://www.drupal.org/project/yild
+ also check out PoolParty-extension https://www.drupal.org/project/yild_poolparty

14. Yle-API
• On top of our metadata sits an
Yle API-layer (API = application
programming interface)
• Meta-API ties together all Yle-
metadata to a graph
• API-calls in JSON(-LD)
• Compatible with Schema.org, EBUCore och Dublin
Coren
• http://developer.yle.fi/tutorials.html
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15. Roadmap for Yle API
SYND/FYND
DRUPAL 7
Program
s API
Articles
API
Meta
API
Image
API
IMS
Arena
API
Login
API
Weather
API
NewsGuard
ProgramGuide
Finto
Freebase
Scores
APIArpa
Metrics
API

16. The opening of Yle-API
We have taken the first steps to start opening up our API’s
by publishing the Programmes API.
When the Articles- and meta-API’s are opened 3rd party
developers can build own versions of most of our services
• http://developer.yle.fi/
tutorials.html

17. Annotation – journalists vs.
algorithms
Journalists:
+ abstraction
+ logic
- inconsistancy in both
quality and quantity
- poor attention to detail
Algortihms:
+ attention to detail
+ consistancy
- lack of human logic
- lack of languag
knowledge - idioms
- great sense of detail

18. Uutisvahti – mobile applikation
News application for pushing news stories through
metadata >> http://yle.fi/uutisvahti/

19. Linked data
We can now link content (data) over borders of:
• Organization
• Publishing systems (CMS)
• Language (language neutral, works with
Finnish, Swedish and English)
• Media type (text, video, audio, images)
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20. Example
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Linking over language
Termsidor
Swedish Finnish

21. Example Recomendations
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22. Background image: CC BY SA http://commons.wikimedia.org/wiki/User:Mschel
• Based on semantic tags
• Easy to add more
attributes like metrics
• Can be improved by
algorithms, like cos-
similarity
• Must be careful to not
make too exact
recommendations >
boring
•  Serendipitet, show the
audience what they didn’t
know they wanted to
know 
RecomendationsExample

23. Linking different media types  new information from
the graph
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Example

24. Background image: CC BY SA http://commons.wikimedia.org/wiki/User:Mschel
How?
It’s really quite simple 
Below note to developer who built it.

25. Example
Media recommendation, when the article was new:

26. Example
The same content after 3 months. The graph lives!

27. Hur?
Still demands quite complicted api-calls must optimize

28. Thank you! Questions?
Mikael ’Micke’ Hindsberg
twitter.com/mickhinds
svenska.yle.fi
utveckling.ylebloggen.fi
www.slideshare.net/mickhin
ds
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