notube project results. bringing tv and web together
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1 New trends in television: social and semantic
Project overview and results
© NoTube project consortium. For re-use see notice at end.
2 New trends in television: social and semantic
TV on the Web: growing trend
3 New trends in television: social and semantic
TV on the Web: channel explosion
4 New trends in television: social and semantic
Source: Nielson Three Screen Report, March 2010
5 New trends in television: social and semantic
From „ Mobile Shopping Framework: The role of mobile devices in the
shopping process” by Yahoo! and the Nielson company, January 2011http://advertising.yahoo.com/industry-knowledge/mobile-shopping-insight.html
6 New trends in television: social and semantic
Including the Web in your TV
Yahoo! launches ConnectedTV platform for Web-based widgets on TV (e.g. Flickr, YouTube, facebook, twitter) – Jan 2009
7 New trends in television: social and semantic
Augmenting TV with the WebBlinkx BBTV makes
video information
and its textual
transcript clickable,
and links to Web
sources such as
IMDB and Wikipedia
www.blinkxbbtv.com
Also Mozilla has a
project on showing
content around
videos using HTML5
www.drumbeat.org
8 New trends in television: social and semantic
Some Web-TV solutions today
Stand alone boxes such as
• TiVo – original DVR, added on-demand video, YouTube, music and photos from the Web
• Boxee – STB offering its own store of apps
• AppleTV – relaunched as $99 product tied to iTunes content, and iPhone/iPad integration
+ Hybrid boxes tied to specific IPTV providers
+ Games consoles (Sony, Microsoft, Nintendo) also adding Internet and video services to TV!
9 New trends in television: social and semantic
Some Web-TV solutions today
First TVs with
integrated Web
and individual
app platforms
in 2011.
Future TVs will
be „connected“
as standard. LG SmartTV, pic courtesy
http://www.wired.com/gadgetlab/2011/01/lg-smart-tv/
10 New trends in television: social and semantic
State of the art in TV
• TV content shifting to the Web as delivery platform
– An explosion in available content at any time
• Web content shifting to the TV as augmentation of the TV experience
– An explosion in additional content at any time
11 New trends in television: social and semantic
Limitations of today‘s TV
• Too much content in one place
– How to find what you want to watch? Sort between live TV, TV on demand, archives, video portals and P2P-TV
• Too much functionality at any one time
– The whole Internet while you watch TV. But what do viewers really want to be able to do additionally (parallel) to watching TV?
12 New trends in television: social and semantic
Social TV
• Integrate the TV experience with the so-called Social Web
– Who are my friends and what do they watch?
– What do my friends like -> maybe I‘d like it too
– Where are my friends now -> connect via the shared TV experience
• Key goal for social TV
– Enhance my TV experience through my friends‘ TV experience
13 New trends in television: social and semantic
Semantic TV
• Add formal semantic descriptions for
– TV programmes
– TV schedules (EPGs)
• Link those descriptions to other semantic data on the Web, cf. Linked Data
• Two key use cases for semantic TV:
– Filtering of TV content -> personalisation, recommendation
– Augmentation of TV content with Web data
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NoTube project
• Integrating TV & Web with help of semantics
– Open and interlink TV content in a Web fashion with Linked Open Data
• Putting the user back in the driving seat
– Connect multitude of distributed personal data with explicit semantics
• TV is not bound to the device– Computer as TV & vice versa– Mobile device as remote control
15 New trends in television: social and semantic
NoTube partners
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Bridging Web and TV cultures
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Rest of this slideset
• Technological background (Semantic Web, Linked Data)
• Semantic annotations for TV data (semantic TV)
• Extracting knowledge from my activities and social graph (social TV)
• TV content recommendation (personalized TV)
• The further future: finally … interactive TV
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“If computers can understand the
meaning behind the information
they can
learn what we are interested in
and
better help us find what we want.”*
* Source: http://www.slideshare.net/HatemMahmoud/web-30-the-semantic-web
(1) Semantic Web, Linked Data
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The Semantic WebThe vision of what was termed the “Semantic Web“ first came to public
attention through an article in Scientific American in May 2001.
* Source: T. Berners-Lee, J. Hendler, O. Lassila; “The SemanticWeb”, Scientific American, 284(5):34–43, May 2001.
“The Semantic Web is not a separate
Web but an extension of the current one,
in which information is given well-defined
meaning, better enabling computers and
people to work in cooperation.”*
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HTMLHTML was too limited for Web documents – it is purely a presentation
format. The tags in HTML have no meaning outside how content should be
rendered in the browser, and so the meaning of the content must be
interpreted by a human, hence excluding any possibility of machine
processing.
<u>James Bond</u>
<b>MI5</b><br>
Her Majesty's Secret
Service<br>
Secret HQ<br>
<i>007 England</i><br>
James Bond MI5
Her Majesty's
Secret
Service
Secret HQ
007 England
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XML
<name>James Bond</name>
<company>
<shortname>MI5</shortname>
<fullname>Her Majesty's Secret
Service</fullname>
<address><street>Secret HQ</street>
<postcode>007</postcode>
<country>England</country>
</address>
</company>
The core idea of XML – Extensible Markup Language – is to provide for
definitions of markup which allows self-describing tags, i.e. tags which
describe the meaning of the content they mark up rather than its
presentation
James Bond MI5
Her Majesty's
Secret
Service
Secret HQ
007 England
22 New trends in television: social and semantic
RDFRDF provides a graph structure for making statements about things.
Individual things, and not just files, are given an URI identifier.
This is where the Semantic Web begins.
from is a child element of flight
(syntactic structure)
http://my.org/flightAI288
:from http://my.org/Vienna
:to http://my.org/Innsbruck
:dep 01-01-2009T12:00
:arr 01-01-2009T12:55
:price „88“
:currency http://my.org/euro
<flight>Flight AI288
<from>Vienna</from>-
<to>Innsbruck</to>
dep <dep>1.1. 1200</dep>
arr <arr>1.1. 1255</arr>
price <price>88€</price>
</flight>
from is a property of the resource
http://my.org/flightAI288
23 New trends in television: social and semantic
RDFSRDF Schema begins to formalise the meaning of things spoken about in
RDF on the basis of computational logic. RDFS permits simple ontologies
(models about concepts and their properties) to be defined, which can be
used to conclude new knowledge.
http://my.org/Vienna
is a http://my.org/City
http://my.org/City
subClass of http://my.org/PopulatedPlace
http://my.org/Vienna
is a http://my.org/PopulatedPlace
http://my.org/flightAI288
:from http://my.org/Vienna
:to http://my.org/Innsbruck
:dep 01-01-2009T12:00
:arr 01-01-2009T12:55
:price „88“
:currency http://my.org/euro
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OWLOWL broadens the possible expressivity of the ontology. This makes
richer models of knowledge about things possible, but at the cost of those
models being more complex for a computer to process.
http://my.org/Vienna
isPlaceIn http://my.org/Austria
http://my.org/Austria
isPlaceIn http://my.org/Europe
isPlaceIn is a transitive property
http://my.org/Vienna
isPlaceIn http://my.org/Europe
http://my.org/flightAI288
:from http://my.org/Vienna
:to http://my.org/Innsbruck
:dep 01-01-2009T12:00
:arr 01-01-2009T12:55
:price „88“
:currency http://my.org/euro
25 New trends in television: social and semantic
SPARQLThe final block of the Semantic Web that we will cover in this introduction is
SPARQL, the query language for semantic data using the RDF data model
(which includes OWL).
http://my.org/flightAI288
:from http://my.org/Vienna
:to http://my.org/Innsbruck
:dep 01-01-2009T12:00
:arr 01-01-2009T12:55
:price „88“
:currency http://my.org/euro
Is there a flight from Vienna to
somewhere in Austria for a price
under 100 euros?
SELECT ?flight
WHERE
?flight :from http://my.org/Vienna
?flight :to ?place
?place :isPlaceIn
http://my.org/Austria
?flight :price ?price
?flight :currency http://my.org/euro
FILTER
(?price < 100)
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Semantic Web principles• Every concept can be identified with URIs
• Resources and relationships are typed semantically
• Partial information is acceptable
• Absolute truth is not necessary
• Evolution as a development principle
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Linked Data principles
• Use URIs as names of things
• Use HTTP URIs so that people can look up those names
• When someone looks up an URI, provide useful information
• Include links to other URIs, so that they can discover more things
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Semantic Web vs Linked Data
“In contrast to the full-fledged Semantic Web vision, linked data is mainly about publishing structured data in RDF using URIs rather than focusing on the ontological level or inference. This simplification - just as the Web simplified the established academic approaches of Hypertext systems -lowers the entry barrier for data providers, hence fosters a widespread adoption.”
vs
- Reference
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Linked Data cloud
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Linked Data for music & TV
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DBPedia: Wikipedia as Linked Data
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DBPedia Mobile
Pictures from revyu.com
Try yourself:
http://wiki.dbpedia.org/
DBpediaMobile
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Resources and representations
http://dbpedia.org/resource/Berlin
.../page/Berlin .../data/Berlin
non-information resource
HTML representation RDF representation
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Linking things, not documents
http://dbpedia.org/resource/ABBA
http://www.bbc.co.uk/music/artists/d87e52c5-
bb8d-4da8-b941-9f4928627dc8#artist
sameAs
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Browsing things, not documents
http://dbpedia.org/resource/ABBA
http://dbpedia.org/resource/Knowing_Me%2C_
Knowing_You..._with_Alan_Partridge
themeMusicComposer
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Asking for things, not documents
Which music artists have composed the theme music for a BBC comedy program?
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(2) Semantic annotation for TV
• What can we annotate in TV?
– The program schedule
– The TV program
– TV program segments
• How can we annotate TV?
– Feature description (low level, analysis based)
– Metadata (date, creator, legal notice)
– Content description (title, summary, genre, concepts)
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Why have metadata?
Archives from where content has to be found and retrieved have been the place where the need for accurate documentation first arose.
39 New trends in television: social and semantic
Broadcast metadata
• Data about data
– All digital resources (A/V, scripts, contracts, reports, pictures, etc.) are data
– Metadata is created at all stages in broadcasting from commissioning to playout
• Three main categories
– Administrative metadata
• Replacing project and asset management paperwork
– Technical metadata
• Format, processing, identification, location, database, network
– Descriptive metadata
• All asset related information, human readable
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Need for common standards
Exchange of information
hampered by lots of proprietary
interfaces
TV Content
Creator 2
TV Content
Creator 3
TV Archive
1
TV Archive
2n+1
NoTubeBroadcaster
1
Broadcaster
2
Broadcaster
3
TV Content
Creator 1
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EPGs
Screenshot http://www.ifanzy.nl
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EPG data
• An EPG is composed of two parts: content descriptions and broadcast description
• Content descriptions contain static data about television programmes such as a brand name (e.g. EastEnders), description or plot summary, type of programme, (e.g. series, movie, news), genre(s) (e.g. drama) actors, directors, recording data, etc.
• Broadcast description is expressed by variable data, such as channel (e.g. BBC ONE), format (e.g. 16:9) and broadcast media (e.g. digital television)
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TVAnytime (1/2)
• Unique document structure
– Program description
– Program location
– Program segmentation
– User description & personalisation
– System aspects
– Content rights
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TVAnytime (2/2)
• Advantages of TV-Anytime
– It is network and middleware independent
– Supports related material, segmentation, locators, group information etc.
• Applications of TV-Anytime
– ARIB
– DVB (MHP, DVB GBS, DVB IPI, DVB CBMS)
– Asian User Groups, Korea
– US’ Consumer Electronic Association
– HbbTV
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TVAnytime schema
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Other models in use
• egtaMETA - a unique metadata exchange schema dedicated for the exchange of ads between ads agencies and broadcasters. NoTube was an early tester of the schema in its personalised advertisements use case.
• BMF – an abstract semantic model designed for metadata exchange in the professional media production domain. ARD in Germany is starting to use BMF.
• Presto Space – format generated by the project of the same name to provide for digital preservation of audiovisual collections. Used by NoTube partner RAI.
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Metadata interoperability via NoTube
http://notube.tv/tv-metadata-interoperability/ for more information
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BBC /programmes
The BBC have made their EPG data machine-readable and published it on the Web
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BBC /programmes: add .rdf
http://www.bbc.co.uk/program
mes/b00rl5y1
http://www.bbc.co.uk/program
mes/b00rl5y1.rdf
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BBC /programmes ontology
From http://purl.org/ontology/po/
This may the first TV content
ontology, but certainly not the
last!
Key organisations in the TV
standards domain are exploring
the publication of metadata in
RDF or SKOS:
• EBU (Core)
• TV-Anytime
• IPTC (NewsML)
The final step must be a
common shared ontology
integrating the different
schemas (cf.W3C Media
Ontology and API)
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Channel identifiers
• Collected resolvable channel identifiers together with relevant metadata in RDF, e.g. 1700+ channel identifiers of Freebase
http://www.cs.vu.nl/~ronny/notube/tv-channels.rdf
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Genre taxonomies
• BBC, TV Anytime, YouTube, IMDB, tvgids.nl …
• Convert them into RDF concepts and define SKOS relations between them, e.g. EBU has done this for the TV Anytime Classification scheme
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Concept extraction
• NLP tools identify named entities in text and attach an unique identifier to them
e.g. OpenCalais, Zemanta
• Focus on key classes of entity such as person, place or organisation
• Use of Linked Data for common concept identifiers
• Ontotext developed specifically for TV metadata the tool LUPedia
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LUPedia (http://lupedia.ontotext.com)
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Concept extraction for TV
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Linking TV content to Web content
starring
David Dickinson
Tim Wonnacott
birthplace
Barnstaple
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Pause
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(3) Extracting knowledge about the user
Idea: generating user profiles from data the user creates on the Social Web, and in this way facilitating a personalised TV experience without an intrusive user profiling process.
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Facebook, Twitter & co.
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Activity Streams
• RSS/Atom feeds include a title, description, link and some other metadata;
• Activity Streams extend this with a verb and an object type
– to allow expression of intent and meaning
– to provide a means to syndicate user activities
• Supported by Facebook, MySpace, Windows Live, Google Buzz and…
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Getting TV into the Social Network„ BBC iPlayer adds
Twitter and Facebook to socialise TV”
– Share what you are watching on iPlayer
– Sync viewing with friends
– Real time chat
Techcrunch Europe, May 26 2010
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TV viewer actions
• Recorded
• Consumed
• Loved
• Bookmarked
• …
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Twitter activity
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Bringing it all together
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Eurovision example
• Analyse tweets with the #eurovision tag over a set time period (during the program)
• Extract country and positive/negative remark
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Getting the user‘s interests
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Beancounter architecture
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FOAF
• RDF based format
– Defines properties for describing a person and their relations to other people and objects
http://xmlns.com/foaf/spec/
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Weighted Interests
• Add weighting to the foaf:interest property
See http://xmlns.notu.be/wi/
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FOAF as common vocabulary
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Beancounter web UI
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Collecting user streams
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Viewer profile (1/2)
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Viewer profile (2/2)
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(4) TV content recommendation• Recommender strategy
– Collaborative recommendation
• You share interests with your friends
• Statistical analysis: what content is liked/watchedquantitively more by others with similar interests/history
– Content-based recommendation
• An interest in X means a potential interest in Y
• Pattern-based analysis: what content has related conceptsto the content liked/watched by you
– Hybrid recommendation
• Best of both!
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NoTube recommendationapproach
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Recommendation lifecycle
Graphic by Libby Miller, BBC
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Linked Data recommendations
• The content-based approach:
– Identify weighted sets (patterns) of DBPediaresources from user activity objects
– Compute distance between DBPedia concepts in the user profile and in the program schedulethrough its SKOS-based categorisation scheme
– Choose the matches above a certain threshold forTV programme recommendation
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User interests (DBPedia concepts)
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Match user interest and TV subjects
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N-Screen http://n-screen.notu.be
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Get recommendations
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TV recommendation calculation
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So, is this the future of television?More: http://notube.tv/showcases/personalised-news/
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Or this?More: http://notube.tv/showcases/tv-guide-and-adaptive-ads/
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Or this?More: http://notube.tv/showcases/tv-and-the-social-web/
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And in the farther future?
89 New trends in television: social and semantic
Interested in the project results?
Find out more online at www.notube.tv
All contents © NoTube project 2009-2012
No re-use of any slides or content of slideswithout explicit acknowledgement of:
NoTube project, www.notube.tv &
this slideset, www.notube.tv/slides