On Quora, I answered the questions, “How do knowledge graphs work? How can knowledge graphs improve the current state of artificial intelligence?” Here I’m reposting and adding a bit to those answers.

First, some context on the term “knowledge graph”

Knowledge graphs are foundational and can be used for many different purposes. It’s important first to realize that every answer you’ll get on a Q&A forum such as Quora will reflect the answerer’s bias. And that bias will be tied to the use case(s) and industry or industries that answerer has familiarity with.

I’ve seen an answer on Quora that refers to Google’s Knowledge Graph (KG), for example. That knowledge graph is unique and tied to Google’s unique place in web search. In that sense, Google’s KG is not representative of a knowledge graph a given reader will be building.

For my answer here, I’m going to rely on the insights of a couple of different leaders (Andreas Blumauer and Helmut Nagy) at Graphwise, a standards-based knowledge graph platform company that serves many different industries. Andreas and Helmut were together at Semantic Web Company (SWC), a knowledge graph metadata creation management company that merged with Ontotext in 2024. My answer will rely on a book they wrote together called The Knowledge Graph Cookbook while at SWC.

Full disclosure: I do some work for Graphwise that involves explaining how knowledge graphs are used to make AI more capable, reliable and trustworthy.

Key to the use of KGs in artificial intelligence is graph retrieval augmented generation (RAG), a method of enabling probabilistic language model chat interfaces (such as ChatGPT) to retrieve deterministic answers to questions from graph databases. GraphRAG keeps AI accurate by ensuring it retrieves its answers directly from reliable databases rather than just guessing.

I use this GraphRAG Curator site to show how combining the precise symbolic logic in KGs and the power of neural networks creates the foundation for truly dependable AI.

I’ll answer this question first:

How do KGs improve artificial intelligence?

The short answer is that knowledge graphs use context-building metadata (data about data) to place a data source in a context or interrelated contexts where that data can be reused.

In other words, if you’re building knowledge graphs, you’re building a mirrorworld of sorts.

In the business world, a knowledge graph represents parts of the business and how those parts of the business relate to one another so that content and data together can be shared and reused across the business, including how AI can assist in that sharing and reusing effort.

Here’s how Andreas and Helmut say KGs improve AI and other IT systems as well:

• Knowledge graphs (KGs) solve well-known data and content management problems.

• KGs are the ultimate linking engine for enterprise data management.

• KGs automatically generate unified views of heterogeneous and initially unconnected data sources, such as Customer 360.

• KGs provide reusable data sets to be used in analytics platforms or to train machine learning algorithms.

• KGs help with the dismantling of data silos. A semantic data fabric is the basis for more detailed analyses

(from p. 21 of The Knowledge Graph Cookbook)

Keep in mind that statistically-oriented machine learning of an AI system is a black box (a neural network that doesn’t explain itself) with an input and an output. What that circumstance implies is that the output is entirely determined by the input data. If all you’re feeding the black box is garbage, all you’ll get on the output side will be garbage. Garbage in, garbage out.

In that sense, knowledge graphs improve AI by fixing AI’s data quality problem.

Quality KGs give AI a solid foundation by using semantic metadata. This is essentially a “digital map” that tells the AI exactly how different people, places, and ideas are connected, providing the context it needs to understand the real world.

In a standard database, metadata usually just describes the “container” (e.g., this file is 5MB or this column is for dates). In a semantic KG, metadata is the key to self-connecting context from domain to domain. The metadata describes the relationships and meaning of the data points.

Machines ideally help humans build that knowledge and data foundation, with humans leading the effort.

By building a good, relevant and updated knowledge graph for the input side of your AI system, you’re directly improving the quality of an AI’s output.

Now, to the first question:

How do knowledge graphs work?

In a nutshell, Andreas and Helmut explain that there’s a conceptual model, a linguistic model and a data graph. KGs provide explanatory models of aspects of the world (a department in the business world and how it functions, for example) so that AIs and humans together can operate in what becomes a described, articulated mirrorworld of annotated structured and less structured data.

• Ontology: conceptual model

• Taxonomy: linguistic model

• Data Graph: instance data and metadata; documents and annotations

(p. 92 of The Knowledge Graph Cookbook)

For more information on how knowledge graphs work, see “What is a Knowledge Graph?”

The linked page explains as follows:

“The heart of the knowledge graph is a knowledge model: a collection of interlinked descriptions of concepts, entities, relationships and events. Knowledge graphs put data in context via linking and semantic metadata and this way provide a framework for data integration, unification, analytics and sharing.”

The linked page also explains that not every data graph is a knowledge graph, and not every knowledge graph does reasoning, or inference. The powerful, semantic standards-based knowledge graphs are built to provide reasoning ability across the graph and simplify integration and interoperation between graphs—thus solving the data management dilemma enterprises face.

For more information:

Andreas Blumauer and Helmut Nagy, “The Knowledge Graph Cookbook: Recipes That Work,” PoolParty Semantic Suite, accessed March 18, 2026, https://www.poolparty.biz/the-knowledge-graph-cookbook.

Graphwise, “What Is a Knowledge Graph?,” Graphwise Fundamentals, accessed March 18, 2026, https://graphwise.ai/fundamentals/what-is-a-knowledge-graph/.

Graphwise, “What Is a Large Language Model?,” Graphwise Fundamentals, accessed March 18, 2026, https://graphwise.ai/fundamentals/what-is-large-language-model/.