IRS support for OWL-S and WSMO
This event took place on Monday 21 June 2004 at 12:30
Dr Farshad Hakimpour KMi, The Open University
There is a great deal of work on developing ontologies (such as OWL-S and WSMO) for describing semantics of Web Services. IRS is one of the few existing systems (if not the only one) to support the Semantic Web Services technologies. I will show how the underlying model of IRS (i.e. Task-PSM ontology) could support parts of OWL-S, as well as how we modified and extended the task-PSM ontology to be able to support features of OWL-S. I will also present further capability the Task-PSM ontology provides, in comparison to OWL-S. I will briefly describe the status of the current work for supporting WSMO.
Download PowerPoint Presentation (614 KB ZIP file)
This event took place on Monday 21 June 2004 at 12:30
Dr Farshad Hakimpour KMi, The Open University
There is a great deal of work on developing ontologies (such as OWL-S and WSMO) for describing semantics of Web Services. IRS is one of the few existing systems (if not the only one) to support the Semantic Web Services technologies. I will show how the underlying model of IRS (i.e. Task-PSM ontology) could support parts of OWL-S, as well as how we modified and extended the task-PSM ontology to be able to support features of OWL-S. I will also present further capability the Task-PSM ontology provides, in comparison to OWL-S. I will briefly describe the status of the current work for supporting WSMO.
Download PowerPoint Presentation (614 KB ZIP file)
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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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