Invited talk at CrowdSem 2013 workshop held at Internatonal Semantic Web Conference (ISWC 2013), Sydney, 21st October 2013

1. Big Data meets Big Social
Social Machines
and the Semantic Web
David De Roure

2. 1. Big Data meets Big Social: Introducing
the Fourth Quadrant
2. Theory and Practice of Social Machines
3. Bringing a Social Machines Perspective
to Semantic Web Projects
4. Bringing a Semantic Web Perspective
to Social Machines Projects

3. Christine Borgman

4. BioEssays,, 26(1):99–105, January 2004
First
http://research.microsoft.com/en-us/collaboration/fourthparadigm/

5. This is a Fourth Quadrant Talk
More machines
cyberinfrastructure
Semantic Grid
Big Data
Big Compute
The Fourth
The Future!
Conventional
Computation
Social
Networking
Quadrant
More people
Online R&D
Science 2.0

6. Nigel Shadbolt et al

7. More machines
The Social and the Machine
Machines empowered
by people e.g.
mechanical turk
People empowered
by machines
e.g. collective action
More people

8. Edwards, P. N., et al. (2013) Knowledge Infrastructures: Intellectual Frameworks and
Research Challenges. Ann Arbor: Deep Blue. http://hdl.handle.net/2027.42/97552

9. ontology.com

10. 1. Big Data meets Big Social: Introducing
the Fourth Quadrant
2. Theory and Practice of Social Machines
3. Bringing a Social Machines Perspective
to Semantic Web Projects
4. Bringing a Semantic Web Perspective
to Social Machines Projects

11. The Order of Social Machines
Real life is and must be full of all kinds of
social constraint – the very processes
from which society arises. Computers
can help if we use them to create
abstract social machines on the Web:
processes in which the people do the
creative work and the machine does the
administration… The stage is set for an
evolutionary growth of new social
engines.
Berners-Lee, Weaving the Web, 1999

12. Some Social Machines

13. SOCIAM: The Theory and Practice
of Social Machines
• Southampton
Shadbolt, Hall, Berners-Lee,
Moreau
• Edinburgh
Robertson, Buneman
• Oxford
De Roure, Lintott, OII
http://www.sociam.org/

14. http://gow.epsrc.ac.uk/NGBOViewGrant.aspx?GrantRef=EP/J017728/1
• Research into pioneering methods of supporting
purposeful human interaction onBehaviour Wide Web, of
the World is socially
the kind exemplified by phenomena such as Wikipedia and
constituted, not
Galaxy Zoo.
programmed in
• These collaborations are empowering, as communities
identify and solve their own problems, harnessing their
commitment, local knowledge and embedded skills,
without having to rely on remote experts or governments.
• The ambition is to enable us to build social machines that
solve the routine tasks of daily life as well as the
emergencies… to develop the theory and practice so that
we can create the next generation of decentralised, data
intensive, social machines. We are interested in design
• Understanding the attributes of the current generation of
successful social machines will help us build the next.

16. Image
Classification
Talk
Forum
Citizen Scientists
data reduction
Scientists

17. Building a Social Machine
Virtual World
(Network of
social interactions)
Model of social interaction
Participation and
Data supply
Design and
Composition
Physical World
(people and devices)
Dave Robertson

18. Composing Social Machines
“The myExperiment social machine protected by the reCAPTCHA
social machine was attacked by the spam social machine so we
built a temporary social machine to delete accounts using people,
scripts and a blacklisting social machine then evolved the myExp
social machine into a new social machine…”

19. • Serendipitous assembly
• Bot or not?
• Social Machines are being
observed by Social Machines
Cat De Roure

20. https://support.twitter.com/entries/18311-the-twitter-rules

21. http://webscience.org/wstnet-laboratories/

24. 1. Big Data meets Big Social: Introducing
the Fourth Quadrant
2. Theory and Practice of Social Machines
3. Bringing a Social Machines Perspective
to Semantic Web Projects
4. Bringing a Semantic Web Perspective
to Social Machines Projects

25. The Problem
signal

understanding
INT
.
VERSE
VERSE BRIDG VERSE BRIDG VERSE O .
E
E
UT

26. Some Social Machines of
Music Information Retrieval
Annotation
machine
Internet
Archive
MusicBrainz
Recommenders
http://archive.org/details/etree
http://musicbrainz.fluidops.net/
http://www.music-ir.org/mirex/
http://www.ismir.net/
Mirex
Machine
ISMIR Machine
Peer review

27. SALAMI
23,000 hours of
recorded music
Digital Music
Collections
Student-sourced
“ground truth”
Music Information
Retrieval Community
Community
Software
Supercomputer
Linked Data
Repositories

28. Ashley Burgoyne

29. salami.music.mcgill.ca
Jordan B. L. Smith, J. Ashley Burgoyne, Ichiro Fujinaga, David De Roure, and J. Stephen
Downie. 2011. Design and creation of a large-scale database of structural annotations. In
Proceedings of the International Society for Music Information Retrieval Conference,
Miami, FL, 555–60

30. Segment Ontology
class structure
Ontology models properties from musicological domain
• Independent of Music Information Retrieval research and
signal processing foundations
• Maintains an accurate and complete description of
relationships that link them
Ben Fields, Kevin Page, David De Roure and Tim Crawford (2011) "The Segment Ontology: Bridging Music-Generic and DomainSpecific" in 3rd International Workshop on Advances in Music Information Research (AdMIRe 2011) held in conjunction with IEEE
International Conference on Multimedia and Expo (ICME), Barcelona, July 2011

31. Music Information Retrieval Evaluation eXchange
MIREX TASKS
Audio Onset Detection
Audio Beat Tracking
Audio Tag Classification
Audio Chord Detection
Audio Tempo Extraction
Audio Classical Composer ID
Multiple F0 Estimation
Audio Cover Song Identification Multiple F0 Note Detection
Audio Drum Detection
Query-by-Singing/Humming
Audio Genre Classification
Query-by-Tapping
Audio Key Finding
Score Following
Audio Melody Extraction
Symbolic Genre Classification
Audio Mood Classification
Symbolic Key Finding
Audio Music Similarity
www.music-ir.org/mirex
Audio Artist Identification
Symbolic Melodic Similarity
Downie, J. Stephen, Andreas F. Ehmann, Mert Bay and M. Cameron Jones. (2010). The Music Information
Retrieval Evaluation eXchange: Some Observations and Insights. Advances in Music Information Retrieval Vol.
274, pp. 93-115

32. Meandre
seasr.org/meandre

35. Stephen Downie

36. SALAMI results: a living experiment and a music observatory

37. 1. Big Data meets Big Social: Introducing
the Fourth Quadrant
2. Theory and Practice of Social Machines
3. Bringing a Social Machines Perspective
to Semantic Web Projects
4. Bringing a Semantic Web Perspective
to Social Machines Projects

38. More machines
That big picture again
Big Data
Big Compute
Social
The Future!
Conventional
Computation
Social
Networking
Machines
More people

39. Big data elephant versus sense-making network?
Iain Buchan
The challenge is to foster the co-constituted socio-technical
system on the right i.e. a computationally-enabled sensemaking network of expertise, data, models and narratives.

40. Intersticia, for Web Science Australia

41. 1. Design of new algorithms and
interfaces
2. New approaches to distributed
inference and query
3. Developing declarative social
machinery, including policyaware systems of privacy, trust
and accountability
4. “Humanity in the loop”
J. Hendler, T. Berners-Lee, From the Semantic Web to social machines: A research challenge
for AI on the World Wide Web, Artificial Intelligence (2009), doi:10.1016/j.artint.2009.11.010

42. Coupling and Composing Social
Machines
It’s an ecosystem… and Semantic
Web is the glue
• See ISWC workshops!
• Policy, privacy, trust and
accountability
• Provenance
• Data integration
Social Machines are co-constituted
• Social Media Analytics
• Linkage versus anonymisation
• Social Science of Social Machines

43. Building a Social Machine
How do we make
building successful
social machines as
reliable as building
successful websites?
What are the
components/service
s/utilities
and how are they
assembled?
How are they
instrumented and
monitored?

44. Semantic Workflow
Steffen Staab et al. Neurons, Viscose
Fluids, Freshwater Polyp Hydra and SelfOrganizing Information Systems. Journal
IEEE Intelligent Systems Volume 18
Issue 4, July/August 2003 Page 72-86
• OWL-S, SWS, … virtual organisations revisited?
• Back office versus human playground

45. Web as
lens
Web as artifact
Web Observatories
http://www.w3.org/community/webobservatory/

46. Towards a socio-technical
system of observatories
Technical and business interface
observatory

47. Social
Knowledge
Objects
Descriptive
layer
Observatories
Knowledge
Infrastructure

48. Scholarly Machines
Ecosystem

49. Research Objects
www.researchobject.org
Jun Zhou

50. Closing thoughts
1. The future is Big Data and Big Social… and with
increasing automation (there be dragons!)
2. The Theory, Practice, Design and Construction of
Social Machines are emerging areas of study
3. You are knowledge infrastructure and Social Machines
designers… it may be useful to think about your
projects in terms of Social Machines
4. Think about Semantic Web plus Social Machines for
tomorrow’s knowledge infrastructure: policy,
provenance, composition, social objects

51. david.deroure@oerc.ox.ac.uk
www.oerc.ox.ac.uk/people/dder
@dder
Slide credits: Christine Borgman, Elena Simperl, Paul Edwards, Ontology,
Nigel Shadbolt, Dave Robertson, Ichiro Fujinaga, Ashley Burgoyne, Kevin Page,
Stephen Downie, Iain Buchan, Jun Zhou
Thanks to the SOCIAM and SALAMI teams, and to Sean Bechhofer, TBL, Christine
Borgman, Carole Goble, Jim Hendler, Chris Lintott, Megan Meredith-Lobay, Kevin
Page, Ségolène Tarte, Jun Zhou and colleagues in DH@Ox, e-Research South,
FORCE11, GSLIS, myExperiment, myGrid, Smart Society and Wf4Ever
SOCIAM: The Theory and Practice of Social Machines is funded by the UK Engineering and
Physical Sciences Research Council (EPSRC) under grant number EPJ017728/1 and
comprises the Universities of Southampton, Oxford and Edinburgh. See sociam.org.
Research also supported in part by Wf4Ever (FP7-ICT ICT-2009.4 project 270192),
e-Research South (EPSRC EP/F05811X/1), Digital Social Research (ESRC RES-149-34-0001A), Smart Society (FP7-ICT ICT-2011.9.10 project 600854).
http://www.slideshare.net/davidderoure/social-machines-and-the-semantic-web

52. Social Machines
Web Science Trust
Zooniverse
SALAMI
MIREX
myExperiment
Research Objects
Wf4ever
FORCE11
Ontology
http://sociam.org
http://webscience.org
https://www.zooniverse.org
http://salami.music.mcgill.ca
http://www.music-ir.org/mirex
http://www.myexperiment.org
http://www.researchobject.org
http://www.wf4ever-project.org
http://www.force11.org
http://ontology.com
W3C Community Groups:
ROSC
http://www.w3.org/community/rosc
Web Observatory http://www.w3.org/community/webobservatory