An Evaluation Framework for Semantic Metadata
This event took place on Wednesday 24 October 2007 at 11:30
Yuangui Lei KMi, The Open University
Because poor quality semantic metadata can destroy the effectiveness of semantic web technology by hampering applications from producing accurate results, it is important to have frameworks that support their evaluation. However, there is no such framework developed to date. In this context, we proposed i) an evaluation reference model, which sketches some fundamental principles for evaluating semantic metadata, and ii) an evaluation framework, which provides a set of instruments to support the
detection of quality problems and the collection of quality metrics
for these problems. In this talk, I will present the reference model and evaluation framework.
(Due to upgrading of webcasting facility this event will not be available as a webcast or a replay)
This event took place on Wednesday 24 October 2007 at 11:30
Because poor quality semantic metadata can destroy the effectiveness of semantic web technology by hampering applications from producing accurate results, it is important to have frameworks that support their evaluation. However, there is no such framework developed to date. In this context, we proposed i) an evaluation reference model, which sketches some fundamental principles for evaluating semantic metadata, and ii) an evaluation framework, which provides a set of instruments to support the
detection of quality problems and the collection of quality metrics
for these problems. In this talk, I will present the reference model and evaluation framework.
(Due to upgrading of webcasting facility this event will not be available as a webcast or a replay)
Future Internet
KnowledgeManagementMultimedia &
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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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