Knowledge is complicated and so is its representation.

Knowledge is complicated and so is its representation, especially when trying to store it in a computer. One approach to solve this issue is by organizing bodies of knowledge in neat graph structures. A project that seeks to model knowledge in this way is the MMT language\cite{rabe2013scalable}, which has theory graphs as its semantics. Unfortunately the graph structures which are generated represent the knowledge so well that they inherent its complexity.

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Since these theory graphs can be massive in size, it should not be surprising that like any other large project they can be plagued by redundancies and entirely unnecessary dependencies.

For this reason it should be no surprise that these theory graphs can be plagued by redundancies and entirely unnecessary dependencies.

To help reduce unnecessary complexity of these theory graphs, I introduce Styler, an automated tool for optimizing theory inclusions.

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To help reduce unnecessary complexity of these large theory graphs, I introduce Styler, an automated tool for optimizing theory inclusions.

The goal of this tool is to automatically detect possible optimizations that reduce the number of edges without breaking the graph and recommend them to the user.