Multi-Agent Ontology Mapping framework Based on Evidence Theory for a Question Answering System
This event took place on Monday 06 June 2005 at 12:30
Miklos Nagy KMi, The Open University
In my presentation I will introduce an experimental multi agent ontology-mapping framework in the AQUA query answering system that incorporates uncertainty handling inherent to the mapping process. The framework uses Dempster-Shafer theory of evidence for dealing with incomplete and uncertain information produced by the different similarity mapping algorithms. A novel approach is presented how specialized agents with partial local knowledge of the particular domain achieve ontology mapping without creating global or reference ontology. Our approach is particularly suitable fit for a query-answering scenario, where answer needs to be created in real time that satisfies the query posed by the user.
The talk is being hosted by Dr. Maria Vargas-Vera from KMi.
This event took place on Monday 06 June 2005 at 12:30
In my presentation I will introduce an experimental multi agent ontology-mapping framework in the AQUA query answering system that incorporates uncertainty handling inherent to the mapping process. The framework uses Dempster-Shafer theory of evidence for dealing with incomplete and uncertain information produced by the different similarity mapping algorithms. A novel approach is presented how specialized agents with partial local knowledge of the particular domain achieve ontology mapping without creating global or reference ontology. Our approach is particularly suitable fit for a query-answering scenario, where answer needs to be created in real time that satisfies the query posed by the user.
The talk is being hosted by Dr. Maria Vargas-Vera from KMi.
Future Internet
KnowledgeManagementMultimedia &
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We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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