Context Graph vs. Knowledge Graph: What’s the Difference?

What is a context graph, and how is a context graph different from a knowledge graph? In this discussion, Amy Hodler of GraphGeeks and Ravi Marwaha of Arango explore context graphs vs. knowledge graphs and why the distinction matters for enterprise AI, AI agents, and trusted business context. Ravi explains how a knowledge graph represents entities and relationships, while a context graph extends that foundation with temporal state, trust, provenance, auditability, and explainability. The conversation explores why these additional layers become increasingly important as AI moves from answering questions to agents that reason, decide, and act using enterprise data. The session also dives into bi-temporal data, shared context for AI agents, incremental graph updates, semantic meaning, and the importance of maintaining an accurate version of business reality as enterprise information changes over time. Ravi then walks through Arango's Contextual Data Platform, including AutoGraph and AutoRAG, showing how structured and unstructured data can be transformed into a knowledge graph and contextual data layer. The demo shows how retrieval can surface citations, source documents, embeddings, graph relationships, provenance, and decision traces to make AI-generated answers more explainable and verifiable. What You'll Learn 🧠 The difference between a context graph and a knowledge graph 🔗 Why entities and relationships alone are not enough for enterprise AI ⏳ How temporal and bi-temporal context helps AI understand what was true and when 🔍 Why provenance, lineage, auditability, and explainability matter for trusted AI 🤖 Why AI agents need shared, governed business context 🧩 How structured and unstructured enterprise data can become a contextual data layer ⚙️ How AutoGraph automates knowledge graph creation 🚀 How AutoRAG supports retrieval, validation, citations, and more complex reasoning Chapters 00:00 Introduction 01:37 Why Context Matters for Enterprise AI 03:34 Context Graph vs. Knowledge Graph 05:48 Do We Need Context Graphs Without AI? 07:02 Why Relationships Alone Are Not Enough 10:39 The Four Pillars of a Context Graph 13:15 Bi-Temporal Data and Context Graphs 15:35 Semantic Meaning, Trust, and Explainability 19:15 Connecting Enterprise Data and Business Meaning 22:10 Knowledge Graph, Context Graph, and Semantic Layer 24:41 Q&A: Updating Temporal Data 26:17 Q&A: How Arango Handles Temporal Data 28:17 Automating Knowledge Graphs for AI 32:40 Automating RAG for AI Agents 33:25 AutoGraph and AutoRAG 37:46 Demo: AutoGraph and AutoRAG 40:23 Building a Corpus Graph 42:23 Configuring Retrieval and Ontologies 44:07 Incremental Graph Updates 45:00 AutoRAG, Retrieval, and Provenance 48:52 Q&A: Bringing Your Own Ontology 51:14 Q&A: Synonyms and Ontologies 53:17 Q&A: Rules and Deterministic Logic 55:46 Arango and the Multimodel Data Stack 58:23 Final Thoughts 59:57 Closing Topics Covered Context Graphs Knowledge Graphs Enterprise AI Agentic AI AI Agents Business Context Contextual AI Bi-Temporal Data Temporal Graphs Knowledge Graph Automation AutoGraph AutoRAG GraphRAG RAG AI Explainability AI Provenance AI Governance Contextual Data Platform Learn More 🌐 Website: https://arango.ai 📖 Documentation: https://docs.arango.ai 💻 GitHub: https://github.com/arangodb 🔗 LinkedIn: https://www.linkedin.com/company/arangodb/ 📅 Upcoming webinars and events: https://arango.ai/events/ Subscribe for more videos on Enterprise AI, Context Graphs, Knowledge Graphs, Agentic AI, GraphRAG, Business Context, and building trusted AI systems.