Identifiers of model elements convey semantics of conceptual models essential to interpretation by human viewers, e.g., the identifier ‘Customer’ labeling an entity type in an Entity-Relationship diagram. Devising meaningful and purposeful identifiers has proven a particular challenge for data modelers, and has repeatedly been observed among the most common difficulties learners of conceptual data modeling face. The Automated Assistant reported in this paper provides data modelers with modeling-time suggestions on identifying and signifying entity types, relationship types, and attributes with meaningful and expedient identifiers. Different from earlier approaches, the Automated Assistant implementation does not rely on fixed reference solutions for modeling tasks, and works with (m)any natural language descriptions of a universe of discourse. We report on the design artifact, the Automated Assistant, highlight two selected design iterations, demonstrate practical applications, and evaluate its performance in typical application cases.
Ternes, Benjamin; Rosenthal, Kristina; and Strecker, Stefan, "AUTOMATED ASSISTANCE FOR DATA MODELERS: A HEURISTICS-BASED NATURAL LANGUAGE PROCESSING APPROACH" (2021). ECIS 2021 Research Papers. 148.
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