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Selected Semantic Web UMBC CoBrA – Context Broker Architecture  Using OWL to define ontologies for context modeling and reasoning  Taking.

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Presentation on theme: "Selected Semantic Web UMBC CoBrA – Context Broker Architecture  Using OWL to define ontologies for context modeling and reasoning  Taking."— Presentation transcript:

1 Selected Semantic Web Projects @ UMBC CoBrA – Context Broker Architecture  Using OWL to define ontologies for context modeling and reasoning  Taking a rule based approach to interpret and reason about context  Using a policy language and engine to control the sharing of user context CoBrA – Context Broker Architecture  Using OWL to define ontologies for context modeling and reasoning  Taking a rule based approach to interpret and reason about context  Using a policy language and engine to control the sharing of user context Rei – Policies for Open and Dynamic Systems  Models deontic concepts of permissions, prohibitions, obligations and dispensations  Uses meta policies for conflict resolution  Uses speech acts for dynamic policy modification  Models different kinds of policies– Security, Privacy, Team formation, collaboration and maintenance, Conversation  Suitable for web services, grid computing, pervasive computing Rei – Policies for Open and Dynamic Systems  Models deontic concepts of permissions, prohibitions, obligations and dispensations  Uses meta policies for conflict resolution  Uses speech acts for dynamic policy modification  Models different kinds of policies– Security, Privacy, Team formation, collaboration and maintenance, Conversation  Suitable for web services, grid computing, pervasive computing Discovering Complex Relationships in the Semantic Web  Design, prototype and evaluate a system supporting the discovery, indexing, querying, and ranking of complex semantic relationships on the Semantic Web.  Capture and utilize provenance and trust to enhance relationship discovery in a distributed environment.  Obtain, prune and evaluate semantic relationships from multiple information sources using provenance and trust.  Explore its practical utility in various domains of interest.  Design, prototype and evaluate a system supporting the discovery, indexing, querying, and ranking of complex semantic relationships on the Semantic Web.  Capture and utilize provenance and trust to enhance relationship discovery in a distributed environment.  Obtain, prune and evaluate semantic relationships from multiple information sources using provenance and trust.  Explore its practical utility in various domains of interest. Integrate multiple social networks Find trust path and propagate trust across social networks from multiple sources Integrate knowledge from multiple sources SWOOGLE 2 SWD Metadata Web Service Web Server SWD Cache The Web Candidate URLs Web Crawler SWD Reader IR analyzerSWD analyzer discovery digest analysis service Ontology Dictionary Swoogle Search Swoogle Statistics Ontology Dictionary  Swoogle is a crawler based metadata and search system for semantic web documents (SWDs) in RDF and OWL.  Swoogle has indexed 230K documents with 37M RDF triples, defining 90K classes, 11K properties and 5.8M instances.  Swoogle is a crawler based metadata and search system for semantic web documents (SWDs) in RDF and OWL.  Swoogle has indexed 230K documents with 37M RDF triples, defining 90K classes, 11K properties and 5.8M instances. http://ebiquity.umbc.edu Research partially supported by NSF, DARPA, IBM


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