As building ontologies manually is extremely labor-intensive and time-consuming, there is great motivation to automate the process.
Relevant terms can be determined, e.g., by calculation of the TF/IDF values or by application of the C-value / NC-value method.
In the subsequent step, similarly to coreference resolution in information extraction, the OL system determines synonyms, because they share the same meaning and therefore correspond to the same concept.
A further method for the derivation of a concept hierarchy exists in the usage of several patterns that should indicate a sub- or supersumption relationship.
Instead, bootstrapping methods are developed, which learn these patterns automatically and therefore ensure broader coverage.
This can be achieved, e.g., by analyzing the syntactic structure of a natural language definition and the application of transformation rules on the resulting dependency tree.
In this step, the OL system tries to extend the taxonomic structure of an existing ontology with further concepts.
During frame/event detection, the OL system tries to extract complex relationships from text, e.g., who departed from where to what place and when.