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BOEMIE: Bootstrapping Ontology Evolution with Multimedia Information Extraction
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 develop 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.
S. Melzer, C. Gröpl, J. Muntel, and D. Becher.
Improving peptide identification in proteome analysis by retention time and secondary structure filtering approaches.
14th International Conference on Research in Computational Molecular Biology, 2010.
K. Gronau, J. Muntel, S. Melzer, M. Hecker, and D. Becher.
Quantitation of phosphorylated proteins via metabolic labeling and 2D-Page.
18th International Mass Spectrometry Conference, 2009. .
S. Espinosa Peraldi, A. Kaya, S. Melzer, R. Möller, and M. Wessel.
Towards a Media Interpretation Framework for the Semantic Web.
The 2007 IEEE/WIC/ACM International Conference on Web Intelligence (WI`07), 2007.
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S. Espinosa, A. Kaya, S. Melzer, R. Möller, M. Wessel.
Towards a Foundation for Knowledge Management: Multimedia Interpretation as Abduction.
In Proc. of the International Workshop on Description Logics DL'07, 2007.
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Silvana Castano, Sofia Espinosa, Alfio Ferrara, Vangelis Karkaletsis, Atila Kaya, Sylvia Melzer, Ralf Möller, Stefano Montanelli, Georgios Petasis.
Ontology Dynamics with Multimedia Information: The BOEMIE Evolution Methodology.
In Proc. of the International Workshop on Ontology Dynamics (IWOD), 2007.
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D. Kosmopoulos Y. Pratikakis V. Gatos S. Perantonis G. Petasis P. Fragou V.
Karkaletsis K. Biatov C. Seibert S. Espinosa S. Melzer A. Kaya R. Moeller
S. Petridis, N. Tsapatsoulis. Project Work:
Deliverable D2.1 Methodology for Semantics Extraction from Multimedia Content, December 2006.
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Sylvia Melzer and Ralf Möller.
How sensor data interpretation could benefit from description logics.
In Proc. of the 2006 International Workshop on Description Logics DL'06, 2006.
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Master Thesis:
A content-based publish-subscribe architecture for individualized sensor data supply (in German)
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2005
Prediction of tertiary protein structure for proinsulin precursor (in German)
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