3 Septembre - 9 Septembre

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Lundi 3 Septembre
Heure: 16:00 - 17:30
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: Analysing large-scale Research Data with Semantic Technologies
Description: Francesco OSBORNE Semantic Technologies provide useful solutions for the analysis of big scholarly data since they facilitate the integration of large datasets and support tasks such as Natural Language Processing and Information Retrieval. In particular, ontologies that describe research topics and their relationships proved to be effective tools for making sense of research dynamics, classifying publications, detecting research communities, and forecasting research trends. However, these knowledge bases are very expensive to craft and tend to become obsolete fairly quickly.
In my talk, I will discuss the automatic approach that we designed to generate and update the Computer Science Ontology (CSO), a large-scale ontology of research topics including about 25K concepts. CSO has been used for supporting Springer Nature in classifying editorial products, informing marketing decisions, and evolving their internal taxonomy. I will present some systems adopting this knowledge base and describe their effect on the workflow of a major publishing company. I will also discuss the advantage of combining Machine Learning and Semantic Technologies for addressing complex tasks such as predicting research trends and forecasting technology migrations.