Considering the ambiguity present in scholarly articles, approaches are necessary for the disambiguation of entities and relations using various contextual and multimodal cues to reliably assign entities to explicitly defined types. Further, to tackle data heterogeneity, the defined types need to be related to each other through domain knowledge expressed in a formal language. These objectives could benefit from ontologies which are formal, shared conceptualizations of domains . Ontologies allow for incorporating domain-specific knowledge as axioms in description logic. This renders such knowledge machine-interpretable, effectively enabling logical reasoning, improving semantic interoperability, and providing utility for data harmonization. Thus, when structured extraction of information is concerned, a common demand is that it should conform to an existing ontology . Adhering to a pre-defined ontology allows for merging and reuse of datasets -which were previously semantically-annotated in rigorous efforts -in new contexts to address a variety of different objectives. However, to comply with the demand of conforming to a pre-defined ontology, a language model needs to reliably assign a class label to entities in text. This so-called named entity recognition (NER) is a fundamental subtask when transforming unstructured information contained in text into structured knowledge and the main scope of the work at hand.