Ontological Engineering Group (OEG) at Universidad Politécnica de Madrid (UPM) meets CCC at UniTO

Where: Sala Conferenze Terzo Piano, dipartimento di Informatica
When: Starting from h 15

Title: NLP and Knowledge Graphs – Carlos Badenes-Olmedo (UPM)
Abstract: Showcase of the results of research and innovation carried out in our research group, where we have combined natural language processing techniques with information based on knowledge graphs. Recent advances will be highlighted, emphasizing how this union has facilitated the exploration of new perspectives and creative solutions for complex data analysis. The contribution of this research will be discussed in the broader context of data science, illustrating how research in this area can move forward.

Bio: Carlos Badenes-Olmedo is an Assistant Professor at the Universidad Politécnica de Madrid (UPM), Spain, and a research member of the Ontological Engineering Group (OEG). His research on advanced techniques for knowledge extraction from unstructured data combines machine learning, natural language processing and Knowledge Graphs. Carlos is also co-founder of the company, a technology-based spin-off that facilitates the exploration of large document corpora

h 15.40
Title: Formalising political speech with ontologies, an approach to exploit the discourse of different parties in different media over time. – Ibai Guillén Pacho (UPM)
Bio: Ibai Guillén Pacho is a PhD candidate in Artificial Intelligence and a member of the Ontology Engineering Group (OEG) at the Universidad Politécnica de Madrid. The same university where he completed his Master’s Degree in Artificial Intelligence (2022). He is a graduate in Computer Engineering and Digital Business Transformation by the University of Deusto (2021). His thesis is currently focused on natural language processing tasks, more specifically in the field of diachronic content analysis. His main areas of interest are content analysis, language models and knowledge representation. ORCID

h 16.20
Title: O-Dang! The Ontology of Dangerous Speech Messages– Marco Stranisci (Dipinfo, UniTO)

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.

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