Keynote Speakers

TBA

 

Madjid Fathi is a professor and Head of KBS & KM (Knowledge Based System & Knowledge Management) institute at the EECS Department at the University of Siegen, Germany. He obtained his M.Sc. degree in Computer Science and Ph.D. degree (Dr.-Ing.) both from the University of Dortmund, Germany, in 1986 and 1991, respectively. Accordingly, he obtained Habilitation degree (Post-Doctorate) at the University of Ilmenau, Germany, in 1998. Before he got the Professor at the Department of Electrical Engineering and Computer Science at the University of Siegen he was visiting scholar at Florida State University and from 2003 at LMM (Lab for Micromechanics - Prof. Garmestani) Georgia Institute of Technology.

 

Since 2004, he is in Siegen. He was Visiting Scholar with Professor Zadeh father of Fuzzy Logic at U.C. Berkeley dept. of EECS joined the BISC (Berkeley Initiative of Soft Computing) from Sep/2012 to Sept/2013. As head of KBS he leads a large academic team of researchers and educators which has, thus far, resulted in over 90 theses. His research interests are focused on AI, Knowledge Based Systems (KBS), knowledge management and their applications in medicine and engineering, knowledge transfer, organizational learning, and knowledge discovery from text (KDT).

 

He is the editor of "Integration of Practice-Oriented Knowledge Technology" (2013) and "Integrated Systems, Design and Technology" (2011) published by Springer, as well as three text books (the last one has been published in October 2019 with the title: Computer-Aided Writing by Springer) and five edited books. He, with his students, has published with more than 270 publications including 30 Journal publications, and obtained four paper awards. He got the European Award Cut-e prize 2015. He is a senior member of IEEE as well as member of editorial board of five respective journals. He is the founder of Alzheimer Knowledge Platform.

From Documents to Knowledge Graph – New Avenues for Document Driven Knowledge Graph Construction

 

Knowledge Graph is a powerful tool for knowledge management, search, and information retrieval from structured and unstructured data. In industrial applications, this requires technological progress on the one side, and the skills of integration processes of document content from different areas on the other side. In addition to the content to be retrieved automatically from databases, human experience from employees, in the form of lessons learned, must also be considered. The handling of different data types, the possibility of storing larger capacities of data in the graph must be ensured and, above all, the representation of the document contents must occur in a suitable form. For the knowledge representation of knowledge graphs, the Multidimensional Knowledge Representation (MKR) technique can be used as a tool to integrate text analysis results into the knowledge graph. This approach will be presented in the talk together with other document-centric variants of knowledge graph construction and explained on the basis of different projects.