O-Dang! The Ontology of Dangerous Speech Messages
Marco A. Stranisci, Simona Frenda, Mirko Lai, Oscar Araque, Alessandra, T. Cignarella, Valerio Basile, Viviana Patti, Cristina Bosco

TL;DR
This paper introduces O-Dang!, an ontology and knowledge graph for organizing Italian linguistic datasets on dangerous speech, supporting interoperability and a perspectivist approach to annotations.
Contribution
It presents a systematic ontology and knowledge graph for integrating and annotating dangerous speech datasets, accommodating multiple perspectives and labels.
Findings
Organized Italian datasets into a structured knowledge graph.
Supported encoding of multiple annotation perspectives.
Analyzed offensiveness across different corpora.
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…
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Taxonomy
TopicsNatural Language Processing Techniques · Semantic Web and Ontologies · linguistics and terminology studies
MethodsOntology
