Abstract
In this paper, an extended method for ontology learning from concept maps is presented. Concept maps are a flexible and informal knowledge representation, while ontologies are semantically formalized representations oriented to be processing by intelligent systems. The mapping between them is a formal transformation based on semantics inference in concept maps. OpenCyc and domain ontologies are used in a combined way with WordNet for increasing the coverage in the semantics inference in the concept map and the method’s applicability. The novel proposal is experimentally evaluated using concept maps from published works with satisfactory and promising results.
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