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Transcript of 1 Towards Decentralized Communities and Social Awareness Pierre Maret Université de Lyon (St...
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Towards Decentralized Communities and Social Awareness
Pierre Maret
Université de Lyon (St Etienne)Laboratoire Hubert CurienCNRS 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
Expressed using web semantic techniques : OWL
{ ki } knowledge{ bi } behavior
{ ki } knowledgeTulip is_a FlowerRed is_a ColorTulip has_property RedT1 instance_of Tulip
{ bi } behaviorSend messageReceive messageExtract InstancesSet Value
{ ki } knowledge{ bi } behavior
Actor
Actor
Actor
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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/knowledge 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 CommunitiesAn 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 communitiesC1 (on Car)C2 (on Flower)(Owner)
{ ki }Tulip is_a FlowerC1 is a CommunityC2 is a Community //joint communitiesC2 (on Flower)
{ ki } Tokyo is_a City//joint communitiesC1 (on Car)
A1
A2
A3
A3 has previously joined A1's community on Flowers. A3 wants to send some info to this communityA2 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