  Ontologies are a standard tool for creating semantic schemata in many
knowledge intensive domains of human interest. They are becoming increasingly
important also in the areas that have been until very recently dominated by
subsymbolic knowledge representation and machine-learning (ML) based data
processing. One such area is information security, and specifically, malware
detection. We thus propose PE Malware Ontology that offers a reusable semantic
schema for Portable Executable (PE - the Windows binary format) malware files.
This ontology is inspired by the structure of the EMBER dataset, which focuses
on the static malware analysis of PE files. With this proposal, we hope to
provide a unified semantic representation for the existing and future
PE-malware datasets and facilitate the application of symbolic, neuro-symbolic,
or otherwise explainable approaches in the PE-malware-detection domain, which
may produce interpretable results described by the terms defined in our
ontology. In addition, we also publish semantically treated EMBER data,
including fractional datasets, to support the reproducibility of experiments on
EMBER. We supplement our work with a preliminary case study, conducted using
concept learning, to show the general feasibility of our approach. While we
were not able to match the precision of the state-of-the-art ML tools, the
learned malware discriminators were interesting and highly interpretable.
