Coursera Flash Sale
40% Off Coursera Plus for 3 Months!
Grab it
Explore advanced techniques for reasoning on knowledge graphs in this 51-minute conference talk that examines how structured knowledge representation can enhance AI trustworthiness and grounding. Learn about the fundamental principles of knowledge graph reasoning, including logical inference mechanisms, semantic relationships, and graph-based computational methods. Discover how knowledge graphs serve as a foundation for building more reliable and interpretable AI systems by providing explicit knowledge structures that support transparent decision-making processes. Examine current challenges in knowledge graph reasoning, including scalability issues, incomplete information handling, and integration with neural approaches. Understand the role of knowledge graphs in addressing AI hallucination problems and improving factual accuracy in large language models. Investigate practical applications across domains such as question answering, recommendation systems, and automated reasoning tasks. Gain insights into emerging research directions that combine symbolic reasoning with neural networks to create hybrid AI architectures that leverage both the interpretability of knowledge graphs and the learning capabilities of deep learning models.
