Big Data meets Big Social: Social Machines and the Semantic Web

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

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.

15. Image Classification Talk Forum Citizen Scientists data reduction Scientists

16. 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

17. 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…”

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

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

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

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

23. 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

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

25. Ashley Burgoyne

26. 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

27. 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

28. 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

29. Meandre seasr.org/meandre

30. Stephen Downie

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

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

34. 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.

35. Intersticia, for Web Science Australia

36. 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

37. 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

38. 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?

39. 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

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

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

42. Social Knowledge Objects Descriptive layer Observatories Knowledge Infrastructure

43. Scholarly Machines Ecosystem

44. Research Objects www.researchobject.org Jun Zhou

45. 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

46. 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

47. 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