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In engineering projects involving various parts from global suppliers, onecommon task is to determine which parts are best suited for the projectrequirements. Information about specific parts' characteristics is published inso called data sheets. However, these data sheets are oftentimes only publishedin textual form, e.g., as a PDF. Hence, they have to be transformed into amachine-interpretable format. This transformation process still requires a lotof manual intervention and is prone to errors. Automated approaches make use ofontologies to capture the given domain and thus improve automated informationextraction from the data sheets. However, ontologies rely solely on experiencesand perspectives of their creators at the time of creation and cannotaccumulate knowledge over time on their own. This paper presents ConTrOn --Continuously Trained Ontology -- a system that automatically augmentsontologies. ConTrOn tackles terminology problems by combining the knowledgeextracted from data sheets with an ontology created by domain experts andexternal knowledge bases such as WordNet and Wikidata. To demonstrate how theenriched ontology can improve the information extraction process, we selecteddata sheets from spacecraft development as a use case. The evaluation resultsshow that the amount of information extracted from data sheets based onontologies is significantly increased after the ontology enrichment.

ConTrOn: Continuously Trained Ontology based on Technical Data Sheets and Wikidata