Towards Decentralized Communities and Social Awareness

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Towards Decentralized Communities and Social
Awareness

• Pierre Maret
• Université de Lyon (St Etienne)
• Laboratoire Hubert Curien
• CNRS UMR 5516

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Who I am? Pierre Maret

• PhD in CS (1995)
• Ass. Prof. at INSA Lyon (1998-2007)
• Prof. at Univ of St Etienne (Univ. of Lyon) since
  2008
• Research background DB, IS, electronic
  documents, knowledge management, knowledge
  modeling

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Talk on

• Towards Decentralized Communities and social
  Awareness

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A Community ?

• What is it?
• A set of participants?
• A topic?
• A protocol for the exchange of messages?
• A data base for storing some information?
• Actually, what is/are the objectives?

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Improve information exchanges

• Increase efficiency
• Create new opportunities for relevant exchanges
• Enable exchange of new types of information
• Deliver the right information, at the right
  moment, and to the right person

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Domains addressed

• Knowledge modeling
• Information diffusion, sharing, retrieval
• Recommendation systems

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Social Networks Sites

• Great success
• 4 types
• Content Sharing (i.e. U-Tube)
• Social Notification (i.e. Facebook)
• Expertise Promotion (i.e. Wikipedia)
• Virtual life, games (i.e. Second life)
• Great tools for building communities

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Social Networks Sites

• Regarding Content sharing and Social
  notification
• People trust people they know
• Social network ? Decision making
• Decision making
• to follow recommendations
• to imitate behavior
• to support in real-life activities

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Social Networks Sites

• Social networks can be useful
• but SNS have some drawbacks

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Some drawbacks of SNS

• Multiple registration
• Close world (no interoperability)
• Privacy issues
• No control on data deletion
• Towards a unique governmental secure SNS ? No
• Then what?

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Need for an open approach

• An open approach for community-related
  information exchanges
• include interoperability
• avoid personal data dispersion
• Proposal A community abstraction
• Decentralized bottom-up approach

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Towards a decentralized approach

• 1st step Actors
• 2nd step Communities
• 3rd step Context

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Towards a decentralized approach

• 1st step Actors
• Actors an abstraction to model any participant
• Person
• Personnel assistant (artifact)
• Autonomous system (artifact)
• An actor has
• Knowledge
• Behavior (decision abilities, actions)

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Actors as SW agents

• 2 types of agents
• Context agent
• Dedicated to sensors
• From raw data to information
• Personal agent
• Personal assistant. Pro-active (internal goal)
• Contains some user's knowledge
• Knowledge is "delivered to" and "gathered from"
  the environment
• Mobility scenario or in-office scenario

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Personnel agent

• Role of a user assistant
• Piece of software
• Autonomous software with communication abilities
• Knowledge abstraction of the owner's knowledge
• Decision abilities actions (managed by the
  owner), related to the present knowledge

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Actor abstraction
ki knowledge Tulip is_a Flower Red is_a
Color Tulip has_property Red T1 instance_of
Tulip bi behavior Send message Receive
message Extract Instances Set Value
ki knowledge bi behavior
Actor
Actor
ki knowledge bi behavior
Actor

• Expressed using web semantic techniques OWL

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Making behavior exchangeable

• Knowledge (RDF/OWL ontologies) can be exchanged
• Behavior is generally hardcoded not
  exchangeable
• A model for expressing agent's behavior in SWRL
  (expression of rules on OWL)
• Work of Julien Subercaze (PhD candidate)

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Making behavior exchangeable

• Behavior as a finite state machine
• If (transition from State A to State B)then
  (execute list of actions)

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Describing information

• Using Tags to describe agents information/knowledg
  e
• Tag Annotations, Meta-data
• Concerns any information/knowledge/document
• picture
• signal
• email, etc.

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Tagging activity on personal agents

• Tagging activity
• Automated
• Semi-automated
• Manual
• Useful regarding information retrieval
• Several dimensions/processes for tags
• Location, environmental information, body
  information, thoughts,

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Tagging activity on personal agents

• Work of PhD candidate Johann Stan
• Main idea the meaning of tag changes
  dynamically according to the user and
  circumstances.
• Circumstance
• communities the user belongs to
• context

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2nd step Communities

• 1st Step Actors
• Community A set of actors with compatible
  communication abilities and shared values (common
  domain of interest)
• VKC Virtual Knowledge Communities
• An abstraction for the exchange of information
  in-between actors

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Features for communities

• Community-related knowledge of the agents
• List of (some) communities
• List of (some) agents
• Community-related domain knowledge (about the
  community topic)
• Community-related primitives
• Protocol create, inform, request
• Knowledge selection (extract from its knowledge)
• Knowledge evaluation and insertion (received
  through exchanges)

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Features for communities
Communities
Knowledge
Mappings
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Agent communities

• Community protocol
• Create community (with a topic)
• Join, Leave
• Inform, request
• Specific role (any agents)
• Yellow page
• Knowledge existing communities and topics

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Example
ki //joint communities C1 (on Car) C2 (on
Flower)(Owner)
ki Tulip is_a Flower C1 is a Community C2 is
a Community //joint communities C2 (on Flower)
A1
ki Tokyo is_a City //joint communities C1
(on Car)
A3
A2
A3 has previously joined A1's community on
Flowers. A3 wants to send some info to this
community A2 needs more info about Japan. A2 is
about to create a community on Japan
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Communities and social network

• Memory of interactions builds my social network
• With who?
• The topic?
• The context?
• The environment?
• Carried out with tags
• Used to propose interaction facilities
  (prediction)

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Communities and social network

• Example of annotations of interactions (manual)
• Automatic annotations context, content analysis
• More about the context

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Step 3 Context

• Context data gathered from the environment
• Location
• Internal state
• Environment
• Activity ()
• Situation f(context data)
• SAUPO model
• situation ? communication preferences

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SAUPO modelSituation ? Communication preferences
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Agent's context

• User's current activity as context data
• Identifying the user's current activity to
  promote exchanges
• Event Content analysis and filtering
• Target more accurate solicitations
• Contextual Notification Framework

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Agent's context

• Contextual Notification Framework (Work of Adrien
  Joly, PhD Candidate) ?Filtered ambient awareness
• Main idea
• maintain cooperation in-between people
• while reducing overload
• Context model
• Context sniffer (with user acceptance)
• Matchmaking process (context social network)
  and notification

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Contextual Notification Framework
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Conclusion

• Improving knowledge exchanges
• Used techniques
• Semantics modeling ontologies, owl
• Context awareness
• Social networks
• Leveraged into several scenarios or projects
• Leading idea bottom-up approach

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• Thank you for your attention