Summary
In this episode of the Data Engineering Podcast Professor Paul Groth, from the University of Amsterdam, talks about his research on knowledge graphs and data engineering. Paul shares his background in AI and data management, discussing the evolution of data provenance and lineage, as well as the challenges of data integration. He explores the impact of large language models (LLMs) on data engineering, highlighting their potential to simplify knowledge graph construction and enhance data integration. The conversation covers the evolving landscape of data architectures, managing semantics and access control, and the interplay between industry and academia in advancing data engineering practices, with Paul also sharing insights into his work with the intelligent data engineering lab and the importance of human-AI collaboration in data engineering pipelines.

Announcements

• Hello and welcome to the Data Engineering Podcast, the show about modern data management
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• Your host is Tobias Macey and today I'm interviewing Paul Groth about his research on knowledge graphs and data engineering

Interview

• Introduction
• How did you get involved in the area of data management?
• Can you start by describing the focus and scope of your academic efforts?
• Given your focus on data management for machine learning as part of the INDELab, what are some of the developing trends that practitioners should be aware of?
  • ML architectures / systems changing (matteo interlandi) GPUs for data mangement
• You have spent a large portion of your career working with knowledge graphs, which have largely been a niche area until recently. What are some of the notable changes in the knowledge graph ecosystem that have resulted from the introduction of LLMs?
• What are some of the other ways that you are seeing LLMs change the methods of data engineering?
  • There are numerous vague and anecdotal references to the power of LLMs to unlock value from unstructured data. What are some of the realitites that you are seeing in your research?
• A majority of the conversations in this podcast are focused on data engineering in the context of a business organization. What are some of the ways that management of research data is disjoint from the methods and constraints that are present in business contexts?
• What are the most interesting, innovative, or unexpected ways that you have seen LLM used in data management?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on data engineering research?
• What do you have planned for the future of your research in the context of data engineering, knowledge graphs, and AI?

Contact Info

• Website
• email

Parting Question

• From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

• Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The AI Engineering Podcast is your guide to the fast-moving world of building AI systems.
• Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
• If you've learned something or tried out a project from the show then tell us about it! Email [email protected] with your story.

Links

• INDELab
• Data Provenance
• Elsevier
• SIGMOD 2025
• Digital Twin
• Knowledge Graph
• WikiData
• KuzuDB
  • Podcast Episode
• data.world
  • Podcast Episode
• GraphRAG
• SPARQL
• Semantic Web
• GQL == Graph Query Language
• Cypher
• Amazon Neptune
• RDF == Resource Description Framework
• SwellDB
• FlockMTL
• DuckDB
  • Podcast Episode
• Matteo Interlandi
• Paolo Papotti
• Neuromorphic Computing
• Point Clouds
• Longform.ai
• BASIL DB

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA