Marie-Christine Rousset - Reasoning on Data: Challenges and Applications

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L'équipe des Keynote Speeches : Sihem Amer-Yahia, Jérôme David, Renaud Lachaize
Marie-Christine Rousset (LIG, Université Grenoble Alpes & Institut Universitaire de France)

Marie-Christine Rousset is a Professor of Computer Science at the University of Grenoble Alpes and senior member of Institut Universitaire de France. She is conducting her research at LIG (Laboratoire Informatique de Grenoble) in the SLIDE group. Her areas of research are Knowledge Representation, Information Integration, Pattern Mining and the Semantic Web. She has published over 100 refereed international journal articles and conference papers, and participated in several cooperative industry-university projects. She received  a best paper award from AAAI in 1996, and has been nominated EurAI Fellow (formerly called  ECCAI Fellow) in 2005. She has served in many program committees of international conferences and workshops and in editorial boards of several journals.

[The talk will be given in English]

Réalisation technique : Antoine Orlandi | Tous droits réservés

How to exploit knowledge to make better use of data is a timely issue at the crossroad of knowledge representation and reasoning, data management, and the semantic web.

Knowledge representation is emblematic of the symbolic approach of  Artificial Intelligence based on the development of explicit logic-based models processed by generic  reasoning algorithms that  are founded in logic. Recently, ontologies have evolved in computer science as computational artefacts to provide computer systems with a conceptual yet computational model of a particular domain of interest. Similarly to humans, computer systems can then base decisions on reasoning about domain knowledge. And humans can express their data analysis needs using terms of a shared vocabulary in their domain of interest or of expertise.

In this talk, I will show how reasoning on data can help to solve in a principled way several problems raised by modern data-centered applications in which data may be  ubiquitous, multi-form, multi-source and musti-scale. I will also show how knowlege representation formalisms and reasoning algorithms have evolved to face scalability issues and data quality challenges.

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