What you'll learn

Knowledge graphs are a powerful foundation for AI systems, but their real value comes from what you can discover in them, not just what’s explicitly stored. In this course, Knowledge Graph Algorithms and Reasoning, you’ll learn to apply the algorithms and reasoning techniques that turn connected data into actionable intelligence for search, recommendations, and GenAI-enabled experiences. First, you’ll explore how to traverse knowledge graphs using BFS and DFS, apply shortest path techniques like Dijkstra’s and A* on weighted relationships, and analyze connectivity, cycles, and reachability for multi-hop discovery. Next, you’ll discover how to analyze graph structure using centrality measures and community detection to identify influential entities, clusters, and anomalies that reveal hidden patterns and data quality issues. Finally, you’ll learn how to reason over knowledge graphs using RDFS and OWL semantics, choose between forward chaining and query rewriting strategies, and apply practical techniques for knowledge graph completion, entity resolution, and graph-powered question answering and semantic search. When you’re finished with this course, you’ll have the skills and knowledge needed to traverse, analyze, and reason over knowledge graphs to build more accurate, understandable, and scalable AI systems that can answer complex questions and surface insights across connected data.