\title{Automatically Finding Theory Morphisms for Knowledge Management}
\maketitle
\begin{abstract}
We present a method for finding morphisms between formal theories, both within as well
as across libraries based on different logical foundations.
% These morphisms can yield both (more or less formal) \emph{alignments} between individual symbols as well as truth-preserving morphisms between whole theories.
As they induce new theorems in the target theory for any of the source theory, theory morphisms are high-value elements of
a modular formal library. Usually, theory morphisms are manually encoded, but this
practice requires authors who are familiar with source and target theories at the same
time, which limits the scalability of the manual approach.
To remedy this problem, we have developed a morphism finder algorithm that automates theory
morphism discovery. In this paper we present an implementation in the MMT system and
show specific use cases. We focus on an application of \emph{theory discovery}, where a user can
check whether a (part of a) formal theory already exists in some library, potentially
avoiding duplication of work or suggesting an opportunity for refactoring.
\end{abstract}
\section{Introduction}\label{sec:intro}
\input{intro}
\section{Preliminaries: MMT and Views}\label{sec:prelim}
