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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