NLP techniques associated with the OpenGALEN ontology for semi-automatic textual extraction of medical knowledge: abstracting and mapping equivalent linguistic and logical constructs.
This research project presents methodological and theoretical issues related to the inter-relationship between linguistic and conceptual semantics, analysing the results obtained by the application of a NLP parser to a set of radiology reports. Our objective is to define a technique for associating linguistic methods with domain specific ontologies for semi-automatic extraction of intermediate representation (IR) information formats and medical ontological knowledge from clinical texts. We have applied the Edinburgh LTG natural language parser to 2810 clinical narratives describing radiology procedures. In a second step, we have used medical expertise and ontology formalism for identification of semantic structures and abstraction of IR schemas related to the processed texts. These IR schemas are an association of linguistic and conceptual knowledge, based on their semantic contents. This methodology aims to contribute to the elaboration of models relating linguistic and logical constructs based on empirical data analysis. Advance in this field might lead to the development of computational techniques for automatic enrichment of medical ontologies from real clinical environments, using descriptive knowledge implicit in large text corpora sources.
do Amaral
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MB
Sao Paulo University, Medical School Hospital, Brazil.
Roberts
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Rector
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D001330:Electronic Data Processing; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008037:Linguistics; D008962:Models, Theoretical; D009323:Natural Language Processing; D011871:Radiology; D012660:Semantics; D018875:Vocabulary, Controlled