%0 Conference Paper %1 Moeller2009b %A Möller, Manuel %A Folz, Christian %A Sintek, Michael %A Seifert, Sascha %A Wennerberg, Pinar %B Proc. of the International Conference on Biomedical Ontologies (ICBO) %D 2009 %K medico %T Extending the Foundational Model of Anatomy with Automatically Acquired Spatial Relations %U http://precedings.nature.com/documents/3471/version/1 %X Formal ontologies have gained a lot of 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 THESEUS MEDICO we use the FMA as our main source of background knowledge about human anatomy. Our ultimate goals are to use spatial knowledge about anatomy the FMA to (1) 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 currently available in the FMA. We present a technique for the automatic inductive learning of missing spatial relation instances by generalizing from expert-annotated volume datasets. The result is stored using the formalism of the FMA and subsequently available for spatial reasoning.

@inproceedings{Moeller2009b, abstract = {Formal ontologies have gained a lot of 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 THESEUS MEDICO we use the FMA as our main source of background knowledge about human anatomy. Our ultimate goals are to use spatial knowledge about anatomy the FMA to (1) 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 currently available in the FMA. We present a technique for the automatic inductive learning of missing spatial relation instances by generalizing from expert-annotated volume datasets. The result is stored using the formalism of the FMA and subsequently available for spatial reasoning.}, added-at = {2010-03-12T13:32:29.000+0100}, author = {M\"oller, Manuel and Folz, Christian and Sintek, Michael and Seifert, Sascha and Wennerberg, Pinar}, biburl = {https://www.bibsonomy.org/bibtex/21457112c96209215ff302e6ae7498daf/manuelm}, booktitle = {Proc. of the International Conference on Biomedical Ontologies (ICBO)}, interhash = {301935ed5b1d920b4f04613334092d8e}, intrahash = {1457112c96209215ff302e6ae7498daf}, keywords = {medico}, month = {July}, timestamp = {2010-03-12T13:32:29.000+0100}, title = {Extending the Foundational Model of Anatomy with Automatically Acquired Spatial Relations}, url = {http://precedings.nature.com/documents/3471/version/1}, year = 2009 }