  Document subject classification is essential for structuring (digital)
libraries and allowing readers to search within a specific field. Currently,
the classification is typically made by human domain experts. Semi-supervised
Machine Learning algorithms can support them by exploiting the labeled data to
predict subject classes for unclassified new documents. However, while humans
partly do, machines mostly do not explain the reasons for their decisions.
Recently, explainable AI research to address the problem of Machine Learning
decisions being a black box has increasingly gained interest. Explainer models
have already been applied to the classification of natural language texts, such
as legal or medical documents. Documents from Science, Technology, Engineering,
and Mathematics (STEM) disciplines are more difficult to analyze, since they
contain both textual and mathematical formula content. In this paper, we
present first advances towards STEM document classification explainability
using classical and mathematical Entity Linking. We examine relationships
between textual and mathematical subject classes and entities, mining a
collection of documents from the arXiv preprint repository (NTCIR and zbMATH
dataset). The results indicate that mathematical entities have the potential to
provide high explainability as they are a crucial part of a STEM document.
