Background-based Ontology Mapping
This event took place on Thursday 01 March 2007 at 14:00
Zharko Aleksovski Vrije Universiteit, Amsterdam
Ontology matching is one of the most urgent and important problems on the Semantic Web. In the recent years it became apparent that using existing ontologies to mediate the matching process can have tremendous benefit as compared to the traditional matching methods.
This presentation provides: overview of a framework to perform ontology matching using other ontologies as background knowledge and an insight in matching experiments conducted with existing ontologies. Two ontologies were matched: NALT and Agrovoc, and other six ontologies taken from the Semantic Web were used as background knowledge. The experiments reveal what are the major causes for false matches, and how different characteristics of the background knowledge affect the matching performance.
This event took place on Thursday 01 March 2007 at 14:00
Ontology matching is one of the most urgent and important problems on the Semantic Web. In the recent years it became apparent that using existing ontologies to mediate the matching process can have tremendous benefit as compared to the traditional matching methods.
This presentation provides: overview of a framework to perform ontology matching using other ontologies as background knowledge and an insight in matching experiments conducted with existing ontologies. Two ontologies were matched: NALT and Agrovoc, and other six ontologies taken from the Semantic Web were used as background knowledge. The experiments reveal what are the major causes for false matches, and how different characteristics of the background knowledge affect the matching performance.
Future Internet
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HypermediaNew Media SystemsSemantic Web &
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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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