--- title: "What Is a Metadata Knowledge Graph? | Dawiso" description: "A metadata knowledge graph models data assets, terms, owners, and policies as connected nodes, powering discovery, impact analysis, and AI context. Learn how." url: "https://www.dawiso.com/glossary/metadata-knowledge-graph" language: "en" --- June 10, 2026 metadata knowledge graphknowledge graphGraphRAGactive metadatadata lineageimpact analysisAI context # What Is a Metadata Knowledge Graph? A **metadata knowledge graph** is a [knowledge graph](https://www.dawiso.com/glossary/knowledge-graph) whose entities are an organization's *metadata* - datasets, columns, business terms, owners, policies, reports, pipelines - and whose edges are the meaningful relationships between them: *this column implements this term*, *this report derives from this table*, *this person owns this asset*, *this policy governs this data*. Instead of storing metadata as disconnected records in separate tools, a metadata knowledge graph connects it into a single navigable web that both humans and AI can traverse to understand how everything in the data estate relates. It matters because the most valuable questions about data are *relationship* questions, and relationships are exactly what flat metadata catalogs struggle to answer. "What breaks if I change this column?" "Which governed term does this dashboard metric actually use?" "What sensitive data feeds this AI model?" Each of these is a graph traversal - following edges across assets - and each is nearly impossible to answer when metadata lives as rows in disconnected tables. The metadata knowledge graph turns these from manual investigations into queries, and it is the structure that makes metadata genuinely useful to AI. TL;DR A **metadata knowledge graph** represents data assets, business terms, owners, policies, and processes as *nodes* and their relationships as *edges* - turning disconnected metadata into one connected, traversable web. It answers relationship questions (impact analysis, "which term does this metric use?", "what sensitive data feeds this model?") that flat catalogs cannot. The graph structure is also the ideal substrate for [GraphRAG](https://www.dawiso.com/glossary/graph-rag) and grounded AI, because it gives an AI relationship-aware context, not just isolated facts. It is closely related to [lineage](https://www.dawiso.com/glossary/data-lineage) (a specialised graph of data flow) and underpins a [context layer](https://www.dawiso.com/context-layer). A governed [catalog](https://www.dawiso.com/glossary/data-catalog) that connects its metadata is, in effect, a metadata knowledge graph. ## Metadata Knowledge Graph Defined A knowledge graph represents knowledge as a network of *entities* (nodes) and *relationships* (edges), each typed and meaningful. A *metadata* knowledge graph applies this model specifically to metadata: the nodes are the things a data catalog tracks, and the edges are how they connect. The result is a semantic map of the data estate - not just a list of what exists, but a model of how it all fits together. This is more than a diagram. Because the relationships are explicit and typed, the graph is *queryable*: you can ask it to traverse from a business term to every physical column that implements it, then onward to every report that uses those columns, in a single query. It captures the connective knowledge that normally lives only in experts' heads. ## Nodes & Relationships The power of the model comes from typing both the things and the connections. A typical metadata knowledge graph includes: - **Nodes:** datasets and tables, columns, [business terms](https://www.dawiso.com/glossary/business-glossary), metrics, owners and stewards, reports and dashboards, pipelines, policies, and AI models. - **Edges:** *implements* (column → term), *derives from* (report → table), *owned by* (asset → person), *governed by* (data → policy), *feeds* (table → model), *contains* (table → column). Because each edge has a meaning, the graph encodes genuine knowledge - not just that two things are connected, but *how*. ## Why a Graph, Not a Table Metadata *could* be stored as rows in tables - and traditionally it was. But the questions that matter most about data are about *connections that span many hops*, and relational tables answer those poorly: each hop is another join, and deep or variable-length traversals (follow this term to all downstream reports, however many layers deep) become slow and awkward. A graph is built for exactly this. Traversing relationships is its native operation, so multi-hop questions - impact analysis, root-cause tracing, "everything connected to this" - are natural and fast. This is why [data lineage](https://www.dawiso.com/glossary/data-lineage) is itself a graph: lineage is simply the metadata knowledge graph filtered to the "derives from / flows to" edges. The broader metadata knowledge graph generalises this to *every* kind of relationship, not just data flow. ## What It Powers A metadata knowledge graph is the engine behind capabilities that flat metadata cannot deliver: - **Impact analysis.** Traverse downstream from any asset to see everything a change would affect - before you make it. - **Semantic discovery.** Find data by meaning and relationship, not just keyword - "show me trusted, owned tables related to revenue." - **Grounded AI.** The graph is the ideal substrate for [GraphRAG](https://www.dawiso.com/glossary/graph-rag): instead of retrieving isolated text chunks, an AI traverses relationships to assemble connected, context-rich grounding - far more reliable than flat retrieval. - **Governance reasoning.** Propagate classification and policy along edges - if a source is restricted, everything derived from it inherits the concern. ## How Dawiso Uses It A metadata knowledge graph is what a [data catalog](https://www.dawiso.com/glossary/data-catalog) becomes when its metadata is genuinely connected rather than stored as isolated entries - and that connectedness is core to how Dawiso works. Dawiso harvests metadata from across the estate and links it: business terms to the columns that implement them, assets to their owners, data to the policies that govern it, and sources to the reports and models downstream. Interactive [data lineage](https://www.dawiso.com/product/interactive-data-lineage) is the most visible slice of this graph, but the same connected model powers discovery, impact analysis, and - through the [Context Layer](https://www.dawiso.com/context-layer) and MCP - the relationship-aware context that grounds AI. AI-assisted enrichment helps build the graph by proposing relationships between terms and assets, so the web grows without purely manual effort. The graph is what lets both people and agents ask the relationship questions that matter. ## Conclusion A metadata knowledge graph turns an organization's metadata from a pile of disconnected records into a connected, queryable model of how everything relates. Because it types both the entities and the relationships, it answers the questions that actually matter - impact, provenance, meaning, governance - as fast graph traversals rather than manual archaeology. And because relationships are exactly what AI needs to reason reliably, the graph is the natural foundation for grounded, GraphRAG-style AI. Connect your metadata into a graph, and you get both: people who can finally trace how their data fits together, and AI that understands it. [See it in action](https://www.dawiso.com/context-layer) Dawiso Context Layer Turn raw metadata into a connected, governed knowledge graph your AI can query and trust - served to any agent via MCP. ### Table of contents Metadata Knowledge Graph DefinedNodes & RelationshipsWhy a Graph, Not a TableWhat It PowersHow Dawiso Uses It ## Related Terms ### [What Is a Metadata Lakehouse?](https://www.dawiso.com/glossary/metadata-lakehouse) A metadata lakehouse applies data lakehouse principles to metadata - one unified, open, queryable store for all metadata that powers governance, search, and AI. Read more → ### [What Is a Context Graph?](https://www.dawiso.com/glossary/context-graph) A context graph connects business concepts, data, processes, and people into one relationship model that grounds AI reasoning. Learn how it differs from a knowledge graph. Read more → ### [What Is GraphRAG?](https://www.dawiso.com/glossary/graph-rag) GraphRAG combines knowledge graphs with retrieval-augmented generation to give LLMs richer, relationship-aware context. Learn how it reduces hallucinations and enables complex enterprise Q&A. Read more → ### [What Is Databricks Genie Ontology?](https://www.dawiso.com/glossary/databricks-genie-ontology) Databricks Genie Ontology is a context layer that grounds Genie in business meaning. Learn how Unity Catalog feeds it and where a cross-platform layer reaches. Read more → ### [What Is an Ontology in AI?](https://www.dawiso.com/glossary/ontology-in-ai) An ontology is a formal model of the concepts, properties, and relationships in a domain that AI can reason over. Learn how ontologies power knowledge graphs and grounded AI. Read more → ### [Ontology vs Semantic Layer](https://www.dawiso.com/glossary/ontology-vs-semantic-layer) Ontology vs semantic layer - an ontology models concepts and relationships for reasoning; a semantic layer defines metrics for consistent analytics. Learn the difference. Read more →