Unsupervised Learning of Semantic Relations between Concepts of a Molecular Biology Ontology

In this paper we present an unsupervised model for learning arbitrary relations between concepts of a molecular biology ontology for the purpose of supporting text mining and manual ontology build- ing. Relations between named-entities are learned from the GENIA corpus by means of several stan- dard natural language processing techniques. An in-depth analysis of the output of the system shows that the model is accurate and has good potentials for text mining and ontology building applications.