How to Combine Web2.0 and the Semantic Web
This event took place on Monday 23 April 2007 at 14:00
Tom Heath KMi, The Open University
It is not uncommon to view Web2.0 and the Semantic Web as mutually exclusive, competing paths to the Web of the future, each advocated by a distinct community. We argue that the two approaches are in fact complementary, and that both face challenges the other can solve, such as how to integrate Web2.0 data on a Web scale, and how to enable users to create semantically rich annotations. Here we will use examples from Revyu.com to demonstrate how features of Web2.0 and the Semantic Web can be combined in a single service that overcomes these challenges. Revyu is a Web site where people can review and rate anything they choose. The site is built on Semantic Web technologies, but also uses common features of Web2.0, such as keyword tagging.
This event took place on Monday 23 April 2007 at 14:00
It is not uncommon to view Web2.0 and the Semantic Web as mutually exclusive, competing paths to the Web of the future, each advocated by a distinct community. We argue that the two approaches are in fact complementary, and that both face challenges the other can solve, such as how to integrate Web2.0 data on a Web scale, and how to enable users to create semantically rich annotations. Here we will use examples from Revyu.com to demonstrate how features of Web2.0 and the Semantic Web can be combined in a single service that overcomes these challenges. Revyu is a Web site where people can review and rate anything they choose. The site is built on Semantic Web technologies, but also uses common features of Web2.0, such as keyword tagging.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
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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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