You are here

Frédérique Segond

Research Director, Viseo, France

Frédérique is Director of Research of the Viseo Group and Associated Professor at the Institut National des Langues et Cultures Orientales, in Paris. Before joining Viseo, she was Principal Scientist and manager of the Parsing and Semantics area (ParSem) at the Xerox Research Centre Europe. She joined Xerox as a research scientist in 1993 where she worked on different European and national projects based on natural language technologies. Throughout her research career she has developed, worked and coordinated more than twenty collaborative international research projects links to data understanding.
Frédérique earned a PhD in Applied Mathematics from the Ecole des Hautes Etudes en Sciences Sociales in Paris. She was awarded a PhD grant  from the IBM Scientific Centre In France.  After a one year post-doc at IBM-Watson in Yorktown working on the links between syntax and semantics, she got a research and teaching position at Télécom Paris Sud in charge of starting an activity around Natural Language Processing.
Frédérique is co-author of six books, over 60 scientific papers and 5 patents. She belongs and provides scientific expertise to several scientific national and international committees and institutions. She has been the president of the CONTINT steering committee of the French National Agency for Research (ANR), President of the Association for Computational Linguistics (ATALA). She is now Vice President of the ATALA,  member of the European Language Resources Association board, member of the board of the University Stendhal, member of the Scientific council of the Labex DigiCosme and of the scientific and technical council of the IRT SystemX.

Talk Title: 
The truth of the data pudding is in the integrating
Talk Abstract: 

In this talk we will address the issue of data integration along two dimensions: the one of combining and linking knowledge extracted from data of different kinds residing in different sources. We will provide a quick overview of the state of the art and outline some of the research challenges which still remains to be addressed in order to be able to properly structure, combine and make sense of knowledge encoded in heterogeneous data.  In particular we will insist on the difficulty of defining a generic model to encode these different data into a standard and unified formalism.