  Inside the NLP community there is a considerable amount of language resources
created, annotated and released every day with the aim of studying specific
linguistic phenomena. Despite a variety of attempts in order to organize such
resources has been carried on, a lack of systematic methods and of possible
interoperability between resources are still present. Furthermore, when storing
linguistic information, still nowadays, the most common practice is the concept
of "gold standard", which is in contrast with recent trends in NLP that aim at
stressing the importance of different subjectivities and points of view when
training machine learning and deep learning methods. In this paper we present
O-Dang!: The Ontology of Dangerous Speech Messages, a systematic and
interoperable Knowledge Graph (KG) for the collection of linguistic annotated
data. O-Dang! is designed to gather and organize Italian datasets into a
structured KG, according to the principles shared within the Linguistic Linked
Open Data community. The ontology has also been designed to account for a
perspectivist approach, since it provides a model for encoding both gold
standard and single-annotator labels in the KG. The paper is structured as
follows. In Section 1 the motivations of our work are outlined. Section 2
describes the O-Dang! Ontology, that provides a common semantic model for the
integration of datasets in the KG. The Ontology Population stage with
information about corpora, users, and annotations is presented in Section 3.
Finally, in Section 4 an analysis of offensiveness across corpora is provided
as a first case study for the resource.
