Unsupervised learning of semantic relations between concepts of a molecular biology ontology

Authors:
Massimiliano Ciaramita;Aldo Gangemi;Esther Ratsch;Jasmin Šaric;Isabel Rojas
Affiliations:
Institute for Cognitive Science and Technology, CNR, Roma, Italy;Institute for Cognitive Science and Technology, CNR, Roma, Italy;University of Würzburg, Würzburg, Germany;EML-Research gGmbH, Heidelberg, Germany;EML-Research gGmbH, Heidelberg, Germany
Venue:
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Year:
2005

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Abstract

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 building. Relations between named-entities are learned from the GENIA corpus by means of several standard 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.