lasso context-elicitation-framework- demo
DESCRIPTION
Project LASSO intends to deploy, improve and extend Linked Data (LD) infrastructure in three different use cases. Central feature of all use cases is a lookup service which helps to augment already existing, formalized knowledge with facts from the Linked Open Data (LOD) cloud. These three use cases are Semantic Desktop, Enterprise Collaboration and Inspiration Services. Currently available systems in all of these application domains are sparsely using data from the Semantic Web.http://www.lassoproject.org/TRANSCRIPT
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KnowSe 2.0Context Elicitation Framework
Knowledge Management InstituteTU Graz
Contact: [email protected]
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Motivation
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„Can we build an open source
framework which easily allows to
explore the current situation the user is
in?“
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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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Generic Framework Components
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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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Generic Framework Components
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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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Generic Framework Components
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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
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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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Application Dependend Framework Components
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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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Application Dependend Framework Components
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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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Application Dependend Framework Components
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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
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Wiki Visualization
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Wiki Visualization
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Wiki Visualization
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Wiki Visualization
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Wiki Visualization
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Future work
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• 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