Introduction to Probabilistic Knowledge Graphs: AIDriven Inference and RealWorld Applications

Introduction to Probabilistic Knowledge Graphs: AI-Driven Inference and Real-World Applications
Instructor: Vaibhava Lakshmi Ravideshik

Probabilistic knowledge graphs (PKGs) are transforming the way data is structured and analyzed in AI and machine learning. In this course, AI engineer and author Vaibhava Lakshmi Ravideshik introduces the fundamentals of knowledge graphs and shows how probability theory helps manage uncertainty in data. Through hands-on examples, learn how to construct PKGs, integrate probabilistic reasoning, and apply inference techniques like Markov Chain Monte Carlo (MCMC). Explore real-world applications in decision-making, risk assessment, and predictive modeling, and gain insights into scaling challenges and emerging trends shaping the future of PKGs.

Learning objectives

• Explain the fundamentals of knowledge graphs, including their structure, components, and real-world applications.
• Construct and analyze PKGs by integrating uncertainty into traditional knowledge graphs.
• Use probabilistic inference techniques, such as Markov Chain Monte Carlo (MCMC), to make predictions and decisions in PKGs.
• Identify challenges and future trends in the field of PKGs, such as scalability, data quality, and AI advancements.

Homepage