Towards second and third generation web-based multimedia
Proceedings of the 10th international conference on World Wide Web
Automatic Ontology-Based Knowledge Extraction from Web Documents
IEEE Intelligent Systems
IEEE Intelligent Systems
Semantic facets: an in-depth analysis of a semantic image retrieval system
Proceedings of the 6th ACM international conference on Image and video retrieval
Foundations and Trends in Web Science
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Recommending Tags for Pictures Based on Text, Visual Content and User Context
ICIW '08 Proceedings of the 2008 Third International Conference on Internet and Web Applications and Services
Not all tags are created equal: learning Flickr tag semantics for global annotation
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Document indexing: a concept-based approach to term weight estimation
Information Processing and Management: an International Journal
Trend detection in folksonomies
SAMT'06 Proceedings of the First international conference on Semantic and Digital Media Technologies
An integrated content and metadata based retrieval system for art
IEEE Transactions on Image Processing
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With the increasing amount of multimedia content on the web added as user generated content in Web 2.0 websites, conventional multimedia information retrieval is presented with new challenges. It is no longer possible to rely only on meta-data based retrieval but to consider also content based techniques combined with the collective knowledge generated by users' contributions and geo-referenced meta-data. Tagging is a modest way to annotate such documents and fails to capture a full semantic description of the document content. This report concerns ongoing research to investigate a means to identify, model and utilise semantic descriptions of the user-generated content in Web 2.0 documents using a hybrid approach. The approach consists of three main components, natural language processing, image analysis and a shared knowledge base. In this paper we describe the complete model but, as the image analysis component is in its early stages, the results focus on the natural language processing and the knowledge base. We show that the additional use of these components can improve retrieval and analysis performance over that based only on Web 2.0 tags.