Rule Generation and Matchmaking in Context-Aware System
This event took place on Wednesday 18 March 2009 at 11:30
Dong Liu
Aiming at solving the problem that rules in the existing context-aware systems are manually specified by developers or users, an automatic rule generation method is proposed. Context-aware systems are regarded as decision systems, and context information are reduced with discernibility matrix so as to generate rules. Because data can be utilized are limited, the generated rules can not entirely cover the domains of contexts. The rule precisely matches the current context probably does not exist. An ontology-based rule matching algorithm is presented as the solution to this problem.
This event took place on Wednesday 18 March 2009 at 11:30
Aiming at solving the problem that rules in the existing context-aware systems are manually specified by developers or users, an automatic rule generation method is proposed. Context-aware systems are regarded as decision systems, and context information are reduced with discernibility matrix so as to generate rules. Because data can be utilized are limited, the generated rules can not entirely cover the domains of contexts. The rule precisely matches the current context probably does not exist. An ontology-based rule matching algorithm is presented as the solution to this problem.
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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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