How knowledge drives understanding--matching medical ontologies with the needs of medical language processing.

@article{citeulike:137275, abstract = {In this article, we introduce a knowledge-based approach to medical text understanding. From an in-depth consideration of deep sentence and text understanding we distill basic requirements for an adequate knowledge representation framework. These requirements are then matched with currently available medical ontologies (thesauri, terminologies, etc.). A fundamental trade-off is recognized between large-scale conceptual coverage on the one hand, and formal mechanisms for integrity preservation and conceptual expressiveness on the other hand. We discuss various shortcomings of the most wide-spread ontologies to capture medical knowledge in-the-large. As a result, we argue for the need of a formally sound and expressive model along the lines of KL-ONE-style terminological representation systems in the format of description logics. These provide an adequate methodology for designing more sophisticated, flexible medical ontologies serving the needs of 'deep' knowledge applications which are by no means restricted to medical language processing.}, address = {Computational Linguistics Division-Text Knowledge Engineering Lab, Freiburg University, Germany. hahn@coling.uni-freiburg.de}, author = {Hahn, U. and Romacker, M. and Schulz, S. }, citeulike-article-id = {137275}, issn = {0933-3657}, journal = {Artif Intell Med}, keywords = {classification, medicine, ontology}, month = {January}, number = {1}, pages = {25--51}, posted-at = {2005-12-28 16:12:03}, priority = {2}, title = {How knowledge drives understanding--matching medical ontologies with the needs of medical language processing.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/9930615}, volume = {15}, year = {1999} }

TY - JOUR ID - citeulike:137275 TI - How knowledge drives understanding--matching medical ontologies with the needs of medical language processing. JF - Artif Intell Med VL - 15 IS - 1 SP - 25 EP - 51 AD - Computational Linguistics Division-Text Knowledge Engineering Lab, Freiburg University, Germany. hahn@coling.uni-freiburg.de SN - 0933-3657 N2 - In this article, we introduce a knowledge-based approach to medical text understanding. From an in-depth consideration of deep sentence and text understanding we distill basic requirements for an adequate knowledge representation framework. These requirements are then matched with currently available medical ontologies (thesauri, terminologies, etc.). A fundamental trade-off is recognized between large-scale conceptual coverage on the one hand, and formal mechanisms for integrity preservation and conceptual expressiveness on the other hand. We discuss various shortcomings of the most wide-spread ontologies to capture medical knowledge in-the-large. As a result, we argue for the need of a formally sound and expressive model along the lines of KL-ONE-style terminological representation systems in the format of description logics. These provide an adequate methodology for designing more sophisticated, flexible medical ontologies serving the needs of 'deep' knowledge applications which are by no means restricted to medical language processing. KW - classification KW - medicine KW - ontology AU - Hahn, U AU - Romacker, M AU - Schulz, S PY - 1999/01// UR - http://view.ncbi.nlm.nih.gov/pubmed/9930615 ER -