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| Pinar Wennerberg; Sonja Zillner. |
| Formal knowledge about human anatomy, radiology or diseases is necessary to support clinical applications such as medical image search. This machine processable knowledge can be acquired from biomedical domain ontologies, which however, are typically very large and complex models. Thus, their straightforward incorporation into the software applications becomes difficult. In this paper we discuss first ideas on a statistical approach for modularizing large medical ontologies and we prioritize the practical applicability aspect. The underlying assumption is that the application relevant ontology fragments, i.e. modules, can be identified by the statistical analysis of the ontology concepts in the domain corpus. Accordingly, we argue that most frequently... |
| Tipo: Manuscript |
Palavras-chave: Bioinformatics. |
| Ano: 2009 |
URL: http://precedings.nature.com/documents/3522/version/1 |
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| Pinar Wennerberg; Manuel Möller; Sonja Zillner. |
| To realize applications such as semantic medical image search different domain ontologies are necessary that provide complementary knowledge about human anatomy and radiology. Consequently, integration of these different but nevertheless related types of medical knowledge from disparate domain ontologies becomes necessary. Ontology alignment is one way to achieve this objective. Our approach for aligning medical ontologies has three aspects: (a) linguistic-based, (b) corpus-based, and (c) dialogue-based. We briefly report on the linguistic alignment (i.e. the first aspect) using an ontology on human anatomy and a terminology on radiology. |
| Tipo: Manuscript |
Palavras-chave: Bioinformatics. |
| Ano: 2009 |
URL: http://precedings.nature.com/documents/3521/version/2 |
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| Pinar Wennerberg; Sonja S. Zillner; Alexander Cavallaro. |
| Formal knowledge about human anatomy, radiology or diseases is necessary to support clinical applications such as medical image search. This machine processable knowledge can be acquired from biomedical domain ontologies, which however, are typically very large and complex models. Thus, their straightforward incorporation into the software applications becomes difficult. In this paper we discuss first ideas on a statistical approach for modularizing large medical ontologies and we prioritize the practical applicability aspect. The underlying assumption is that the application relevant ontology fragments, i.e. modules, can be identified by the statistical analysis of the ontology concepts in the domain corpus. Accordingly, we argue that most frequently... |
| Tipo: Presentation |
Palavras-chave: Bioinformatics. |
| Ano: 2009 |
URL: http://precedings.nature.com/documents/3523/version/1 |
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| Pinar Wennerberg; Manuel Möller; Sonja Zillner. |
| To realize applications such as semantic medical image search different domain ontologies are necessary that provide complementary knowledge about human anatomy and radiology. Consequently, integration of these different but nevertheless related types of medical knowledge from disparate domain ontologies becomes necessary. Ontology alignment is one way to achieve this objective. Our approach for aligning medical ontologies has three aspects: (a) linguistic-based, (b) corpus-based, and (c) dialogue-based. We briefly report on the linguistic alignment (i.e. the first aspect) using an ontology on human anatomy and a terminology on radiology |
| Tipo: Manuscript |
Palavras-chave: Cancer; Bioinformatics. |
| Ano: 2009 |
URL: http://precedings.nature.com/documents/3521/version/1 |
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| Manuel Möller; Christian Folz; Michael Sintek; Sascha Seifert; Pinar Wennerberg. |
| Formal ontologies have made significant impact in bioscience over the last ten years. Among them, the Foundational Model of Anatomy Ontology (FMA) is the most comprehensive model for the spatio-structural representation of human anatomy. In the research project MEDICO we use the FMA as our main source of background knowledge about human anatomy. Our ultimate goals are to use spatial knowledge from the FMA (1) to improve automatic parsing algorithms for 3D volume data sets generated by Computed Tomography and Magnetic Resonance Imaging and (2) to generate semantic annotations using the concepts from the FMA to allow semantic search on medical image repositories. We argue that in this context more spatial relation instances are needed than those currently... |
| Tipo: Manuscript |
Palavras-chave: Bioinformatics. |
| Ano: 2009 |
URL: http://precedings.nature.com/documents/3471/version/1 |
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