Knowledge Graphs for Data and AI Applications

Knowledge graphs provide a flexible way to represent and connect data, making them a strong foundation for applications that combine large datasets with AI. In this talk, we will start by defining what knowledge graphs are and why graphs are a natural data model for representing relationships and context. We’ll then look at the specific advantages of graph databases, and how they differ from RDF-based technologies and other graph storage solutions.

The session will also cover practical use cases where knowledge graphs and graph databases add real value, such as integrating heterogeneous data, supporting complex queries, and powering AI-driven applications. Finally, we will discuss how knowledge graphs and graph databases are increasingly used alongside AI, including recent developments like GraphRAG, which extend large language models with structured knowledge.

Referent:
Martin Preusse holds a PhD in Computational Biology and works in the pharmaceutical, life sciences, and healthcare domains. He focuses on building data and AI products that help organizations manage and use complex scientific information. With a background at the intersection of biology and technology, he brings experience in creating platforms that enable research and innovation.