Knowledge Graph Challenges with, without, and for AI
Abstract: Knowledge graphs have nowadays become essential components of many intelligent systems. The flexibility of the graph model and its ability to store data relationships explicitly enables the integration and exploitation of data from very diverse sources, which comes with traditional challenges of data management and query processing but also covers the crucial question of how to provide verifiable knowledge, reliable facts, patterns, and a deeper understanding of the underlying domains. This talk will therefore chart a number of challenges for exploiting graphs to manage and bring meaning to large amounts of heterogeneous data and discuss opportunities with, without, and for artificial intelligence.
Short-Bio: Katja Hose is a full professor at TU Wien's Databases and Artificial Intelligence research unit. She had prior positions at Aalborg University, the Max Planck Institute for Informatics, and received her PhD from Ilmenau University of Technology. Her research is rooted in data and knowledge engineering and focuses on various aspects of graph databases, knowledge graphs, data integration, and applied machine learning. She has co-authored more than 150 peer-reviewed publications and regularly serves as a reviewer for a broad range of conferences including TheWebConf/WWW, VLDB, SIGMOD, EDBT, ISWC, ESWC, etc. She is an editorial board member of the VLDB Journal and the Semantic Web Journal as well as an associate editor for TGDK.