Contents
We are presenting a workshop on behalf of the DTT (Deutscher Terminologie-Tag, the German Terminology Association) that shows how organizations can move from terminology to meaning by building knowledge models for humans and AI. We explain why preferred terms alone are no longer sufficient. Humans and AI systems need shared concepts, controlled vocabularies, context, examples, usage conditions, and explicit semantic structures to interpret, exchange, and reuse information consistently. This includes not only preferred terms, but also admitted and deprecated alternatives and conceptual relations that show how concepts are alike, how they differ, where they are used, and how they fit into a wider knowledge system. In the hands-on part, participants apply these principles to bilingual material, enrich terms with context, review a taxonomy, and build a simple ontology for a chatbot scenario.
Takeaways
Participants learn how context, examples, related concepts, and usage conditions turn terminology into shared meaning for humans and AI.
Prior knowledge
Due to time constraints and the hands-on nature of the workshop, participants must have practical experience in concept-oriented terminology work and in handling terminological entries. They must also know what an AI chatbot is and be able to work with one. Experience with multilingual content and basic knowledge of concept maps or concept systems is recommended.