Application of Domain Ontologies to Natural Language Processing: A Case Study for Drug-Drug Interactions

Natural Language Processing (NLP) techniques can provide an interesting way to mine the growing biomedical literature, and a promising approach for new knowledge discovery. However, the major bottleneck in this area is that these systems rely on specific resources providing the domain knowledge. Domain ontologies provide a contextual framework and a semantic representation of the domain, and they can contribute to a better performance of current NLP systems. However, their contribution to information extraction has not been well studied yet. The aim of this paper is to provide insights into the potential role that domain ontologies can play in NLP. To do this, the authors apply the drug-drug interactions ontology (DINTO) to named entity recognition and relation extraction from pharmacological texts. The authors use the DDI corpus, a gold-standard for the development and evaluation of IE systems in this domain, and evaluate their results in the framework of the last SemEval-2013 DDIExtraction task.