In this paper we present Text2Onto, a framework for on-
tology learning from textual resources. Three main features distinguish
Text2Onto from our earlier framework TextToOnto as well as other
state-of-the-art ontology learning frameworks. First, by representing the
learned knowledge at a meta-level in the form of instantiated model-
ing primitives within a so called Probabilistic Ontology Model (POM),
we remain independent of a concrete target language while being able
to translate the instantiated primitives into any (reasonably expressive)
knowledge representation formalism. Second, user interaction is a core as-
pect of Text2Onto and the fact that the system calculates a con¯dence
for each learned object allows to design sophisticated visualizations of
the POM. Third, by incorporating strategies for data-driven change dis-
covery, we avoid processing the whole corpus from scratch each time
it changes, only selectively updating the POM according to the corpus
changes instead. Besides increasing e±ciency in this way, it also allows
a user to trace the evolution of the ontology with respect to the changes
in the underlying corpus.