Multimedia Knowledge and Social Media Analytics Laboratory

BOEMIE

BOEMIE: Bootstrapping Ontology Evolution with Multimedia Information Extraction, 6th FP IST-STREP, 2006-2008. BOEMIE will pave the way towards automation of the process of knowledge acquisition from multimedia content, by introducing the notion of evolving multimedia ontologies which will be used for the extraction of information from multimedia content in networked sources, both public and proprietary. BOEMIE advocates a synergistic approach that combines multimedia extraction and ontology evolution in a bootstrapping process involving, on the one hand, the continuous extraction of semantic information from multimedia content in order to populate and enrich the ontologies and, on the other hand, the deployment of these ontologies to enhance the robustness of the extraction system.

The ambitious scope of the BOEMIE project and the proven specialized competence of the carefully composed project consortium ensure that the project will achieve the significant advancement of the state of the art needed to successfully merge the component technologies. The main measurable objective of the project is to improve significantly the performance of existing single-modality approaches in terms of scalability and precision. Towards that goal, BOEMIE will deliver a new methodology for extraction and evolution, using a rich multimedia semantic model, and realized as an open architecture. The architecture will be coupled with the appropriate set of tools, implementing the advanced methods that will be developed in BOEMIE.

Furthermore, BOEMIE aims to initiate a new research activity on the automation of knowledge acquisition from multimedia content, through ontology evolution. The resulting technology has a wide range of applications in commerce, tourism, e-science, etc. During the project, the technology will be evaluated through the development of an automatic content collection and annotation service for public events in a number of major European cities. The extracted semantic information will enrich a digital map, which will provide a friendly interface to the end user.

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