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Social Tagging Networks (STN) Leveraging context and social networks. Johann STAN 1,2, Sonia LAJMI 3, PR. Pierre MARET 1, DR. Elod EGYED-ZSIGMOND 3 (PHD Candidate , 1st Year) 1 Alcatel-Lucent Bell Labs France – [email protected]

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social tagging networks stn leveraging context and social networks

Social Tagging Networks (STN)Leveraging context and social networks

Johann STAN1,2, Sonia LAJMI3,PR.Pierre MARET1, DR.Elod EGYED-ZSIGMOND3

(PHD Candidate, 1st Year)

1Alcatel-Lucent Bell Labs France – [email protected]

2Laboratoire Hubert-Curien, Université de Lyon, Saint-Etienne – [email protected]

3LIRIS CNRS Insa de Lyon, Université de Lyon [email protected]

motivations
Motivations
  • There is a need to share photos and to retrieve them
    • Photo album creation
    • Selection of photos according to a given criteria
  • A photo with a name like DSC_0032.jpg is very difficult to retrieve
  • Efficient photo tagging is the main ground for retrieval and sharing
    • Facebook 10 million photos
    • Flickr 2 billion photos and 54 million users
motivations1
Motivations
  • People take more and more photos with mobile devices
  • Mobile devices have a lot of sensors
    • Location, network, accelerometer, …
  • A photo is a meta-data for an event (and not THE event)
    • A trip in a foreign country, a party, a wedding, a dinner, a conference, ….
definition of main concepts
Definition of main concepts
  • Tagging Models
      • Structured according to description schemas
      • Completely free
      • Between the two
  • Tagging Activity
      • Automatic (GEO – Twitter)
      • Semi-automatic (MobileSocialNetwork)
      • Manual
  • Assistance in tagging
      • Keyword recommendation
      • Similar photo recommendation
      • Description schema recommendation
geo twitter
GEO-Twitter
  • Twitter updates are tagged with
    • #GEO: location
    • #Social: your social environment
    • The corresponding template is :

Number of Bluetooth peers

Cell Tower Identifier

hasValue

Automatic Tags

hasValue

Social environment

Location

hasTagType

hasTagType

isTemplate

Ressource

Twitter status message

isA

Template[i]

geo twitter implementation
GEO-Twitter implementation
  • Create local communities
  • Location-related information
  • What is going on in in my neighborhood?
tagging model for social interactions
Tagging Model for Social Interactions

Automatic Tags

Manual Tags

  • Interactions by phone are tagged with:
    • #GEO: location
    • #Social: your social environment
    • #Social Category of the correspondent
    • #Subject / Event in the conversation
    • #Emotional state of the correspondent

isA

Template[j]

isTemplate

Event

Subject

Social Category

hasTagType

Emotional State

hasTagType

hasTagType

Social Environment

hasTagType

hasTagType

Location

hasTagType

Interaction

social interaction analysis
Social Interaction Analysis

Guide the user to explicitly specify the content, context and/or quality of an interaction.

10 | Social Communications | October 2008

the role of context in semantic tagging
The role of context in semantic tagging
  • Related Work deals wih 2 types of context:
    • The context of the photo taking moment
      • PhotoMap, ZoneTag, MMM Image Gallery
    • The context of tagging
      • M. Naaman et coll. 2005
      • B. Shevade, H. Sundaram 2007
      • B. Elliott et Z.M. Özsoyoglu2008
towards semantic tagging models
Towards Semantic Tagging Models

SCOT (Social Semantic Cloud of Tags) [4]

  • Describes the structure and semantics of tagging data, enables social interoperability of tagging data among heterogeneous sources
  • A combination of SCOT, FOAF and Dublin Core to describe tagging activity
towards semantic tagging models1
Towards Semantic Tagging Models

MOAT (Meaning of a Tag)

MOAT (Meaning of a Tag)

(2008, [5])

  • Meaning of a Tag depends on context
  • Introduction of the social aspects of tagging (community, sharing, …)
conclusion of semantic tagging models
Conclusion of Semantic Tagging Models
  • The need for a semantic layer has been clearly identified (similar trend in user modeling community)
  • Several attemps to federate tag ontologies
  • Emerging concepts in tagging models
    • The role of context
    • Social networking and collaborative aspects
  • The issue of how to guide the user in tagging activity
  • User will not tag unless the system immediatly shows a pertinent concept, question…(the user must feel the immediate benefit of tagging)
    • What are the most pertinent concepts to tag according to my context?
    • How can i leverage my social network to increase tagging experience?
  • User Interface Challenge (very important)
scenario
Scenario
  • 25/10/2007: Carole’s birthday
  • She invites collegues and close friends to a night club
  • Bernart takes a photo of the event
  • Marco meets Carole and asks Bernart to share the photo with him
  • The photo is also sent to Alice, Amélie and Carol
slide16

Alice

Amélie

Carole

What: ?

Who: ?

When: 25/12/2007

Where: ?

What: Carole’s birthday

Who/relation: Carole dance

With Alice

Amélie dances with Boris

Activity: Dance Hip Hop

Where: Nightclub Berlin

What: Christmas evening

Where/relation: Beautiful girls dance

Activité: dance Hip Hop

Où: Nightclub Berlin

Witnesses

Amis de Alice

What: party

Where/relation: Alice dances with a girl

Activity: dance

Where: public place

Carole’s friends

Amelie’s friends

What: Carole’s friends

Where/relation: Carole dances with Alice

Activity: dance

Where: public place

slide17

Photo

Interpret

Geographic

Information

Infer social

relationships

Publication

Comment1: y r endow majdouline

Comment2: kiss

Comment3:

Publication

Personalized

tagging

Infer appropriate

tags

WS

User Feedback

User Context

Knowledge

Geographic

Information

ConceptNet

User Calendar

Social Networks

Services Web

Tagger

social relationship inference
Social Relationship Inference

Galery

Scene

night club party

dc:identifier

‘http://www... ‘

dc:type

‘Bernart’

‘Image ‘

foaf:name

dc:author

‘Bernart’

foaf:Person

foaf:phone

displayedBy

‘0049636310166’

foaf:Person

foaf:Person

ref: knowsInPassing

foaf:Person

hasRole

foaf:phone

foaf:Person

foaf:phone

foaf:name

‘photograph’

foaf:phone

‘0049621110100’

‘0049636310166’

‘0033628310142’

foaf:name

post

‘Marco’

‘Carole’

‘y r endow Carole’

Marco

extension of foaf profiles for social networking
Extension of FOAF profiles for social networking

Event: Conference Meeting

Picture with collegues from

different countries

  • Social relationships differ according to the role of the person (witness, tagger, actor, viewer…)
heuristics to increase search in the foaf network
Heuristics to increase search in the FOAF network
  • Association of context and social network categories
    • When at Work, the most probable SNC is the « Professional »
    • When at Home, the most probable SNC is the « Home »
    • ….
conclusion
Conclusion
  • Overview of the application of semantic technologies in tagging
  • There is a need to leverage context and social networks to improve the tagging experience
  • There is a need to provide a mechanism that guides the user in the tagging activity (tagging templates or schemas)
  • The integration of social network, semantics and content annotation has the potential to revolutionize web interaction
  • This leads towards decentralized, but strongly interconnected communities
key references
Key References
  • [1] G. Thomas, “Ontology of folksonomy: A mashup of apples and oranges,” Intl Journal on Semantic Web and Information Systems, 2007.
  • [2] N. Richard , “Tag Ontology,” http://holygoat.co.uk/owl/redwood/0.1/tags, 2005.
  • [3] J.G. Breslin and U. Bojars: “sioc-project.org | Semantically-Interlinked Online Communities.”
  • [4] Hak Lae Kim, J. Breslin, S.K. Yang, H.G. Kim: Social Semantic Cloud of Tag: Semantic Model for Social Tagging. KES-AMSTA: 83-92
  • [5] Alexandre Passant, Philippe Laublet: Combining Structure and Semantics for Ontology-Based Corporate Wikis. BIS 2008: 58-69
  • [6] Jacques Calmet, Pierre Maret, Régine Endsuleit: Agent Oriented Abstraction.
  • Royal Academy of Sciences Journal. Special Issue on Symbolic Computation in Logic and Artificial Intelligence. Vol.98 (1-2). pp.77-84. 2004
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