Learning Term to Concept Mapping Through Verbs: A Case Study

We propose in this paper an approach to learn term to concept mapping with the joint utilization of verb relations and an existing ontology. This is a non-supervised solution that can be applied to any field for which an ontology modeling verbs as relations holding between the concepts was already created. Conceptual graphs, representing a set of verb relations, are learned from a natural language corpus by using part-of-speech information and statistic measures. Labeling strategies are proposed to assign terms of the corpus to concepts of the ontology by taking into account the structure of the ontology and the extracted conceptual graphs. Results of this assignation could be used to automatically create semantic annotations of documents. A first experimentation in the field of accidentology was done and its results are also presented.