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.
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Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.
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