  Logical theories in the form of ontologies and similar artefacts in computing
and IT are used for structuring, annotating, and querying data, among others,
and therewith influence data analytics regarding what is fed into the
algorithms. Algorithmic bias is a well-known notion, but what does bias mean in
the context of ontologies that provide a structuring mechanism for an
algorithm's input? What are the sources of bias there and how would they
manifest themselves in ontologies? We examine and enumerate types of bias
relevant for ontologies, and whether they are explicit or implicit. These eight
types are illustrated with examples from extant production-level ontologies and
samples from the literature. We then assessed three concurrently developed
COVID-19 ontologies on bias and detected different subsets of types of bias in
each one, to a greater or lesser extent. This first characterisation aims
contribute to a sensitisation of ethical aspects of ontologies primarily
regarding representation of information and knowledge.
