Extracting candidate terms from medical texts

In this paper, we present a new method for the construction of domain ontology from texts in the medical field. In our method, NLP tools are not used since our goal is to reduce the amount of noise and the number of filters applied. This allows us to manage the control of semantic quality of Candidate Terms (CT). The proposed method is based on the technique of semantic annotation for controlled terminology extraction via semantic resources, which we supplement with a Coloring Strategy Identification (CSI) method allowing the identification of medical terms and an extraction of CT according to their appearance in the text. In CSI, we exploit the resulting metadata for semantic annotation via some rules. Moreover, we apply a method of categorization by prototype model to overcome perceived silence. The result of this extraction is structured in XML representing the hierarchy of concepts that composes our medical ontology.