Generative AI for data and knowledge engineering

October 4, 2024 - 10:15 - 11:30
Room Black
Generative AI and Foundation models
Value creation in data spaces
Technology Platforms
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Generative AI in general and Foundational Models, such as Large Language Models (LLMs) in particular, offer a vast potential of changing the way we collect, manage, and access both structured and unstructured data. Natural language interfaces will provide new opportunities of interacting with complex, heterogeneuous data volumes, while tedious and costly tasks such as entity linking and reconciliation, or building and populating knowledge graphs will be eased and made more efficient by using LLMs.

In this session, we will shed light current initiatives in the field of using generative AI for data and knowledge engineering. Latest results from European research and innovation projects will be presented, as well as some initial examples of business impact. A panel consisting of representatives from research and industry will discuss challenges and the way ahead for data management in the era of foundational models.


  • Entity linking – enhanced with the power of GenAI: the enRichMyData approach (Roberto/Matteo (SINTEF/UNIMIB?)
  • Interacting with Procurement data using AI – initial results from operations (Ian MakGill, SpendNetwork)
  • The INTEND Approach – Natural languade internaction with data (INTEND Project, TBD)
  • Creating Benchmarks for GenAI for Data Engineering (Francisco Martin Recuerda)?
  • Presentation from DYNABIC (DYNABIC, TBD)
  • InnoGraph and the way ahead for data management, Marko Grobelnik, JSI

Panel – the future of data management

Till Christopher Lech
Research Manager at SINTEF
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