Contextualized Knowledge Repositories for the Semantic Web
This event took place on Tuesday 05 April 2011 at 10:45
Luciano Serafini Data and Knowledge Management, FBK
Though, most of the knowledge available in the Semantic Web is context-dependent, this aspect is not explicitly supported by semantic web representation languages. Some extensions to cope with this limitation have been studied, however, none seems to be satisfactory enough. Rather than extending Semantic Web languages, we propose to fill this gap by tailoring the well established theories of context, developed in the field of AI, to be applicable inside the current Semantic Web languages. In doing this, we take into account the expressivity limitations of RDF/OWL languages, but also the implementability and scalability of the approach within the state-of-the-art triple stores. In this talk, we present a formal definition of a Contextualized Knowledge Repository, its axiomatization, and the description of a first prototypical implementation on top of SESAME, one of the standard Semantic Web triple stores
This event took place on Tuesday 05 April 2011 at 10:45
Though, most of the knowledge available in the Semantic Web is context-dependent, this aspect is not explicitly supported by semantic web representation languages. Some extensions to cope with this limitation have been studied, however, none seems to be satisfactory enough. Rather than extending Semantic Web languages, we propose to fill this gap by tailoring the well established theories of context, developed in the field of AI, to be applicable inside the current Semantic Web languages. In doing this, we take into account the expressivity limitations of RDF/OWL languages, but also the implementability and scalability of the approach within the state-of-the-art triple stores. In this talk, we present a formal definition of a Contextualized Knowledge Repository, its axiomatization, and the description of a first prototypical implementation on top of SESAME, one of the standard Semantic Web triple stores
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Multimedia and Information Systems is...
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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