Semantic models provide a formalized understanding of the contexts of datasets facilitating a
unified interpretation of data by both humans and machines. These models are an essential
component of knowledge graphs and data exchange approaches such as dataspaces. While semantic
models provide unique benefits in data documentation and access the manual creation of
semantic models is a time-consuming and tedious task that requires an in-depth understanding of
semantic technologies. This thesis proposes a semi-automated process for creating semantic
models that emphasizes human involvement alongside the benefits of automation rather than
relying solely on full automation as traditional methods do. The proposed process is
interactive and iterative with manual model refinement playing a central role in improving the
expressiveness and accuracy of semantic models. A central contribution is the design of an
approach to utilize historical semantic models to generate recommendations for model
refinement. This system assists modelers by identifying and suggesting necessary additions to
the semantic models thereby enhancing their completeness and accuracy while promoting model
consistency. A novel semantic modeling platform named PLASMA is presented that focuses on
usability by non-expert modelers and integrates existing automation approaches. Additionally
this thesis examines the integration of large language models into the semantic modeling
process considering their advantages and potential drawbacks. This work represents a step
towards more intuitive and effective tools for semantic modeling especially for users with
limited experience in semantic technologies.
