@inproceedings{stranisci-etal-2022-o,
    title = "{O}-Dang! The Ontology of Dangerous Speech Messages",
    author = "Stranisci, Marco Antonio  and
      Frenda, Simona  and
      Lai, Mirko  and
      Araque, Oscar  and
      Cignarella, Alessandra Teresa  and
      Basile, Valerio  and
      Bosco, Cristina  and
      Patti, Viviana",
    editor = "Kernerman, Ilan  and
      Carvalho, Sara  and
      Iglesias, Carlos A.  and
      Sprugnoli, Rachele",
    booktitle = "Proceedings of the 2nd Workshop on Sentiment Analysis and Linguistic Linked Data",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://aclanthology.org/2022.salld-1.2",
    pages = "2--8",
    abstract = "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 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.",
}