Download - Lasso context-elicitation-framework- demo

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KnowSe 2.0Context Elicitation Framework

Knowledge Management InstituteTU Graz

Contact: [email protected]

09.09.11


Motivation

09.09.11

„Can we build an open source

framework which easily allows to

explore the current situation the user is

in?“


Collect Data

• From different sources• Using various formats• Possible sources?– Sensors, crawlers, agents…– … which provide user related data

• For example:– Software sensors capturing user machine interactions

• e.g. mouse clicks, keyboard input

– Hardware sensors capturing physical conditions• e.g. heartbeat pulse, body temperature

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Infer Knowledge

• Apply context elicication methods• Depend on the application• Take low-level data and generate higher level

concepts• Use knowledge– For further analysis– For visualization

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Architectural Building Blocks

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Generic Framework Components

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Router

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• Concurrent request handling• Data is dispatched for

interested triplificators only• The others remove

themselves from the list of candidates

• As soon as data is available, data is read and dispatched

• One or more interested triplificators possible

• If none is interested connection request is not processed



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Registry

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• Lists the orchestra of available components

• E.g. router, triplificators, elicitation components etc.

• Registration is done upon system start



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Knowledge Base

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• Storage format: RDF• Behind the scenes: Sesame as

RDF Framework API• Modes of editing:

– Triples level– Model level

• Model level supports working with ontologies in an object oriented fashion

• Cross language compiler converts OWL into Java interfaces

• ModelEditor provides a simple way of accessing entities in the ontology



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Notificator

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• Behind the scenes: Equinox implementation of the OSGI Event Administration Service

• Provides sending/receiving notifications– Between bundles– Synchronous/asynchronous

fashion


Application Dependend Framework Components

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Triplificator

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• Accept or deny requests from router– Multithreaded request

processing

• Converts data into triples• Transaction based storage

– If successful then commit– Otherwise rollback

• Broadcasts notifications via Notificator component



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Elicitation Components

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• Get active upon notifications sent by other components

• Execute queries over the knowledge space– Sparql 1.1 Query Language

Support

• Generate higher level concepts



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Sink

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• End of a processing chain• Transfer knowledge outside• “Refinder Sink”:

– Send information about resource similarity

– Via HTTP Post– Payload contains the RDF

graph



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Orchestration

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• Application developer defines– Set of components – Initialization of components– Shutdown of components

• Each application has its own orchestration


Wiki Visualization

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Future work

09.09.11

• Develop first release candidate

• Make it publicly available– as Open Source Software

• We want to provide a ready to use set of components– Including sensors– Visualizations– And more to come

Download - Lasso context-elicitation-framework- demo

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