As part of our brand-new webinar series, our first edition was all about knowledge graphs and the role of Graph Neural Networks (GNNs) in enterprise data science.
Replay our first edition below and subscribe to our webinar channel.
We had the pleasure to hear about the experiences of using knowledge graph technology from industry leaders:
- Anna Gogleva, Associate Director AI Engineer, Biological Insight Knowledge Graph Science Lead at Astrazeneca
- Garima Chaudhary, Head of Financial Crime & Compliance Management Solution Consulting - America at Oracle
- Felix Sasaki, Chief Expert for Knowledge Graph and Semantic Technology to the AI Unit at SAP
- What are knowledge graphs & GNNs
What are some of the definitions out there? What are some of the high-level use cases?
- Key considerations a company should make to benefit from GNNs
How do you identify a knowledge graph problem?
When & where exactly do graphs come into play?
- How to get started with GNNs and use cases that can be exploited
What type of data is needed to solve your business problem?
Why is it important to have a clear understanding of the business problem you’re trying to solve before applying the technology?
What to keep in mind when scaling graphs.
- The influence on the organization’s workflow
How do you bridge the gap between teams to scale knowledge graphs?
The importance of feedback loops to generate predictions.
- The role of accessibility & explainability to communicate graph technology between teams
- An overview of subgraphs
What are the challenges of getting started with subgraphs?
How can you answer specific questions with subgraphs?
- The power the graph brings to the individual and how it helps with decision-making
- The role of graphs in the future data science