  In engineering projects involving various parts from global suppliers, one
common task is to determine which parts are best suited for the project
requirements. Information about specific parts' characteristics is published in
so called data sheets. However, these data sheets are oftentimes only published
in textual form, e.g., as a PDF. Hence, they have to be transformed into a
machine-interpretable format. This transformation process still requires a lot
of manual intervention and is prone to errors. Automated approaches make use of
ontologies to capture the given domain and thus improve automated information
extraction from the data sheets. However, ontologies rely solely on experiences
and perspectives of their creators at the time of creation and cannot
accumulate knowledge over time on their own. This paper presents ConTrOn --
Continuously Trained Ontology -- a system that automatically augments
ontologies. ConTrOn tackles terminology problems by combining the knowledge
extracted from data sheets with an ontology created by domain experts and
external knowledge bases such as WordNet and Wikidata. To demonstrate how the
enriched ontology can improve the information extraction process, we selected
data sheets from spacecraft development as a use case. The evaluation results
show that the amount of information extracted from data sheets based on
ontologies is significantly increased after the ontology enrichment.
