Vendredi 11 mars, 15h00, DS-1950
Machine Learning and Semantic web are covering conceptually different sides of the same story – Semantic Web’s typical approach is top-down modeling of knowledge and proceeding down towards the data while Machine Learning is almost entirely data-driven bottom-up approach trying to discover the structure in the data and express it in the more abstract ways and rich knowledge formalisms. The talk will discuss possible interaction and usage of Machine Learning and Knowledge discovery for Semantic Web with emphases on ontology construction. In the second half of the talk we will take a look at some research using machine learning for Semantic Web and demos of the corresponding prototype systems.
Dunja Mladenić is an expert on study and development of Machine Learning, Data/Text Mining, Semantic Technology techniques and their application on real-world problems. She is associated with the Artificial Intelligence Laboratory of the J. Stefan Institute since 1987. She got her MSc and PhD in Computer Science at University of Ljubljana in 1995 and 1998 respectively. She was a visiting researcher at School of Computer Science, Carnegie Mellon University, USA in 1996-1997 and in 2000-2001. Dunja Mladenic is or was on the Management Board of several European research and development projects. She has published papers in refereed conferences and journals, served in the program committee of international conferences and organized international events in the area of Text Mining, Link Analysis and Data Mining. She is co-editor of several books including “Data Mining and Decision Support : Integration and Collaboration”, Kluwer Academic Publishers 2003, “Semantic Knowledge Management : Integrating Ontology Management, Knowledge Discovery, and Human Language Technologies” Springer 2008, “Web Mining : from Web to Semantic Web”, Springer 2004, “Semantics, Web and Mining” Springer 2006, “From Web to social Web : discovering and deploying user and content profiles”, Springer 2007, “Knowledge Discovery Enhanced with Semantic and Social Information” , Springer 2009.