Finding the underlying schema in knowledge graphs is an imperative operation for various tasks, such as query formulation or exploration. This task becomes even harder, when data are incomplete, noisy or are collected via multiple sources with different schemata that are combined. Several algorithms for extracting an implicit schema from a given knowledge graph have been proposed in the literature. However, the lack of a common framework and evaluation metrics makes it difficult to combine them and compare the results. To fill this gap, we present a modular three-stage framework and we have developed a Python library and web application that performs schema extraction and allows users to visually assess and compare the results. The developed tool, called KGSEC, facilitates experimentation and increases interactivity. Given that the quality of a schema is largely subjective, depending on the user's needs and preferences, KGSEC can make it easier and faster for users to generate a schema that is better tailored to their task.