Structural Isomprphism in Mathematical Expressions: A Simple Coding Scheme

While there exist many methods in machine learning for comparison of letter string data, most are better equipped to handle strings that represent natural language, and their performance will not hold up when presented with strings that correspond to mathematical expressions. Based on the graphical representation of the expression tree, here I propose a simple method for encoding such expressions that is only sensitive to their structural properties, and invariant to the specifics which can vary between two seemingly different, but semantically similar mathematical expressions.