Going Meta: Two Years of Knowledge Graphs

Going Meta's Season 2 offers a comprehensive exploration of knowledge graphs, blending foundational concepts with advanced retrieval techniques to enhance semantic understanding and relevance. The series revisits the explicit semantics of knowledge graphs and the implicit semantics of vector embeddings, emphasizing their complementary roles in creating powerful retrieval-augmented generation (RAG) systems. Episodes delve into sophisticated retrieval methods, such as vector search combined with graph traversals and ontology-driven exploration, while also showcasing the innovative use of large language models (LLMs) in domain modeling and graph schema design. The series highlights Neo4j's dual-graph approach for knowledge graph construction, underscoring the importance of ontologies as structural guides to maintain manageability. As Season 3 approaches, the focus shifts towards AI's consumption, retrieval, and agentic adaptations, continuing the journey of semantic exploration and application.