David De Roure

Big Data meets Big Social

Social Machinesand the Semantic Web

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

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http://research.microsoft.com/en-us/collaboration/fourthparadigm/

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This is a Fourth Quadrant Talk

Big DataBig Compute

Conventional Computation

The Future!

SocialNetworking

cyberinfrastructureSemantic Grid

Online R&DScience 2.0

The Fourth Quadrant

Nigel Shadbolt et al

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The Social and the Machine

Machines empowered by people e.g. mechanical turk

People empoweredby machinese.g. collective action

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

ontology.com

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

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

The Order of Social Machines

Some Social Machines

• SouthamptonShadbolt, Hall, Berners-Lee,Moreau

• EdinburghRobertson, Buneman

• OxfordDe Roure, Lintott, OII

SOCIAM: The Theory and Practiceof Social Machines

http://www.sociam.org/

• Research into pioneering methods of supporting purposeful human interaction on the World Wide Web, of the kind exemplified by phenomena such as Wikipedia and Galaxy Zoo.

• 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.

• Understanding the attributes of the current generation of successful social machines will help us build the next. ht

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Behaviour is sociallyconstituted, notprogrammed in

We are interested in design

Scientists

TalkForum

ImageClassification

data reduction

Citizen Scientists

Physical World(people and devices)

Building a Social Machine

Design andComposition

Participation andData supply

Model of social interaction

Virtual World(Network of social interactions)

Dave Robertson

“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…”

Composing Social Machines

Cat De Roure

• Serendipitous assembly• Bot or not?• Social Machines are being

observed by Social Machines

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

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

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

The Problem

signal

understanding

MirexMachine

Internet Archive

Annotation machine

ISMIR Machine

Peer review

MusicBrainz

Recommenders

Some Social Machines ofMusic Information Retrieval

http://archive.org/details/etreehttp://musicbrainz.fluidops.net/http://www.music-ir.org/mirex/http://www.ismir.net/

Digital Music Collections

Student-sourced “ground truth”

Community Software

Linked Data Repositories

Supercomputer

23,000 hours ofrecorded music

Music InformationRetrieval Community

SALAMI

Ashley Burgoyne

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

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

Segment Ontology

Ben Fields, Kevin Page, David De Roure and Tim Crawford (2011) "The Segment Ontology: Bridging Music-Generic and Domain-Specific" 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

MIREX TASKSAudio Artist Identification 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 Symbolic Melodic Similarity ww

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rg/m

irex

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

Music Information Retrieval Evaluation eXchange

seasr.org/meandreMeandre

Stephen Downie

SALAMI results: a living experiment and a music observatory

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

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That big picture again

Big DataBig Compute

Conventional Computation

The Future!

SocialNetworking

SocialMachines

The challenge is to foster the co-constituted socio-technical system on the right i.e. a computationally-enabled sense-making network of expertise, data, models and narratives.

Big data elephant versus sense-making network?

Iain Buchan

Intersticia, for Web Science Australia

1. Design of new algorithms and interfaces

2. New approaches to distributed inference and query

3. Developing declarative social machinery, including policy-aware 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

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

Coupling and Composing Social Machines

Building a Social Machine How do we make building successful social machines as reliable as building successful websites?

What are the components/services/utilitiesand how are they assembled?

How are they instrumented and monitored?

• OWL-S, SWS, … virtual organisations revisited?• Back office versus human playground

Semantic WorkflowSteffen Staab et al. Neurons, Viscose Fluids, Freshwater Polyp Hydra and Self-Organizing Information Systems. Journal IEEE Intelligent Systems Volume 18 Issue 4, July/August 2003 Page 72-86

Web as lens

Web as artifact

Web as

infrastructure

Web Observatorieshttp://www.w3.org/community/webobservatory/

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observatory Towards a socio-technical system of observatories

SocialKnowledgeObjects

Descriptivelayer

Observatories

KnowledgeInfrastructure

Scholarly MachinesEcosystem

www.researchobject.org

Jun ZhouResearch Objects

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

Closing thoughts

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 ZhouThanks 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 Wf4EverSOCIAM: 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-149-34-0001-A), Smart Society (FP7-ICT ICT-2011.9.10 project 600854).

http://www.slideshare.net/davidderoure/social-machines-and-the-semantic-web

Social Machines http://sociam.org Web Science Trust http://webscience.org Zooniverse https://www.zooniverse.org SALAMI http://salami.music.mcgill.ca MIREX http://www.music-ir.org/mirex myExperimenthttp://www.myexperiment.orgResearch Objects http://www.researchobject.org Wf4ever http://www.wf4ever-project.org FORCE11 http://www.force11.orgOntology http://ontology.com

W3C Community Groups:ROSC http://www.w3.org/community/rosc Web Observatory http://www.w3.org/community/webobservatory