A Framework to Generate Sets of Terms from Large Scale Medical Vocabularies for Natural Language Processing
Salah Ait-Mokhtar, Caroline Hagege, Pajolma Rupi
In this paper we present our ongoing work on integrating large-scale terminological information
into NLP tools. We focus on the problem of selecting and generating a set of suitable terms from the
resources, based on deletion, modification and addition rules. We propose a general framework in
which the raw data of the resources are first loaded into a knowledge base (KB). The selection and
generation rules are then defined in a declarative way using query templates in the query language of
the KB system. We illustrate the use of this framework to select and generate term sets from a UMLS dataset.
CSCT Workshop, University of Potsdam, Germany, March 19th, 2013.