  The need for domain ontologies in mission critical applications such as risk
management and hazard identification is becoming more and more pressing. Most
research on ontology learning conducted in the academia remains unrealistic for
real-world applications. One of the main problems is the dependence on
non-incremental, rare knowledge and textual resources, and manually-crafted
patterns and rules. This paper reports work in progress aiming to address such
undesirable dependencies during ontology construction. Initial experiments
using a working prototype of the system revealed promising potentials in
automatically constructing high-quality domain ontologies using real-world
texts.
