Knowledge Graphs for Contextual AI

Knowledge Graphs for Contextual AI Maya Natarajan Companies are increasingly using artificial intelligence (AI) applications for decision-making today. However, due to lack of context, AI systems have not yet achieved their full potential as reliable solutions for complex problems. Enter knowledge graphs - the modern way to capture relationships and convey their meaning. Knowledge graphs drive intelligence into the data itself and give AI the context to be more explainable, accurate, and repeatable. In this session, you’ll learn how to apply knowledge graphs to enhance every step of AI, from data sourcing and training machine learning models to analyzing predictions and applying results. We’ll walk through the most powerful uses of knowledge graphs in AI systems: data lineage, data discovery and data analytics. Join me to hear how companies like UBS leverage knowledge graphs for data lineage to increase trust and meet regulatory requirements and NASA uses knowledge graphs to map and explore its research documents, making it easier to find mission-critical information such as issues with spacecrafts. Finally, we’ll dive into data analytics to improve forecasts and predictions and touch on how AstraZeneca combines knowledge graphs, graph algorithms and machine learning for patient journey analytics and breakthrough interventions