Nodes

A node is a point in the graph that represents a discrete entity. A person, a company, a product, a document, a disease, or a gene can all be nodes. Each node has one or more labels that describe what kind of entity it is. A node might have the label Person, another might have the label Company, and a third might have the label Product.

Nodes can also carry properties. A Person node might have properties for name, date of birth, and nationality. A Company node might have properties for name, industry, and founding date. Properties are key-value pairs that store data attributes directly on the node.

Edges

An edge connects two nodes and describes the relationship between them. Every edge has a direction, meaning it goes from one node to another, and a type that describes the relationship. Common edge types include works at, founded, acquired, owns, located in, and reports to.

Edges can also carry properties. An edge of type acquired, connecting a company node to another company node, might have properties for the acquisition date and the deal value. This lets the graph store not just that a relationship exists, but the details of that relationship.

Organising Principles

A knowledge graph is more than a collection of nodes and edges. It needs an organising principle, meaning a conceptual framework that gives the graph structure and meaning. This could be a taxonomy that groups products into categories and subcategories, an ontology that defines what types of entities exist and what relationships are valid between them, or a set of business rules that govern how entities connect.

For example, an e-commerce knowledge graph might use a product taxonomy as its organising principle. Products are grouped into categories like Electronics, which contains subcategories like Phones and Laptops. Each product connects to its category through a belongs to edge. This structure lets the graph answer questions like what products are related to this one without scanning every product in the database.

Step 1. Define the Use Case

Start with a focused question. What problem will the knowledge graph solve? Common use cases include recommendation engines, fraud detection, supply chain tracking, entity resolution, and enterprise search. The use case determines what entities and relationships you need to model. A fraud detection graph needs accounts, transactions, devices, and phone numbers as nodes, with edges for transferred money to, shares device with, and shares phone number with. A recommendation graph needs users, products, and categories as nodes, with edges for purchased, viewed, and belongs to.

Choose a narrow starting point rather than modelling the entire domain. A focused scope lets you validate the approach with manageable effort. You can expand the graph later as the use case grows.

Step 2. Model the Graph

Graph modelling means deciding what nodes, edges, and properties your graph will contain. The goal is to represent the domain in a way that makes the important questions easy to answer. If the use case is recommendation, the model needs to support queries like which products did users similar to this one buy. That means the graph needs User nodes connected to Product nodes through purchased edges, and Product nodes connected to each other through shared category or co-purchase edges.

Unlike relational database design, graph modelling is iterative. You start with a rough model, load some data, query it, and refine the model based on what you find. The flexibility of the graph structure makes this iteration cheap. Adding a new relationship type does not require restructuring existing data.

Step 3. Gather and Clean Data

Knowledge graphs are only as good as the data they contain. Data comes from multiple sources, including relational databases, CSV files, APIs, unstructured documents, and third-party data providers. Each source needs to be cleaned and normalised before it can be loaded into the graph.

Entity resolution is the critical step at this stage. The same entity often appears in different sources with different names, formats, or identifiers. A company might be listed as IBM in one database, International Business Machines in another, and IBM Corp in a third. Entity resolution identifies these as the same entity and links them in the graph. Without this step, the graph contains duplicate nodes that fragment the relationship network.

Step 4. Ingest Data

Once data is cleaned and entities are resolved, the next step is to load it into the graph database. This involves creating nodes with their properties, then creating edges with their types and properties, and applying the organising principle. Graph databases typically provide bulk loading tools for large datasets and API-based loading for incremental updates.

Ingestion also involves mapping source data to the graph model. A schema mapping file defines how columns in a source table correspond to node properties and edge types in the graph. For example, a column called company_name in a source table maps to the name property on a Company node, and a column called parent_company_id maps to an edge of type subsidiary_of connecting two Company nodes.

Step 5. Query and Refine

After loading data, test the graph with real queries. Start with the questions defined in the use case. Do the queries return correct results? Are there missing relationships or duplicate nodes? Are the property values accurate? This testing phase often reveals data quality issues that were not visible during cleaning, and the graph model may need adjustment based on what the queries reveal.

GraphRAG

GraphRAG combines knowledge graphs with retrieval-augmented generation. Instead of retrieving text chunks from a vector database, the system retrieves subgraphs from a knowledge graph. A subgraph is a set of nodes and edges that are relevant to the query. The retrieved subgraph is then converted to text and included in the prompt, giving the language model structured relationship data to reason over.

The advantage is precision. When a user asks about the supply chain for a specific product, a GraphRAG system traverses the graph from the product node, following edges to supplier nodes, then to sub-supplier nodes, and returns the full chain as a structured subgraph. The language model receives not just text about suppliers, but the actual relationships between products and suppliers, with edge properties like contract dates and order volumes.

Research from Neo4j and independent studies have shown that GraphRAG can reduce hallucination rates significantly compared to standard text-based RAG. One study reported that GraphRAG made AI agents up to 80 percent more truthful on factual questions, because the structured relationships in the graph provide verifiable grounding that text alone cannot match.

Entity Linking

Entity linking is the bridge between unstructured text and a knowledge graph. Given a piece of text, entity linking identifies mentions of entities and connects them to the corresponding nodes in the graph. When a user asks about Tim Cook, the entity linking system finds the Person node for Tim Cook in the graph and returns its identifier. This identifier lets the system traverse the graph to find related entities, like the company Tim Cook leads and the products that company sells.

Entity linking is what makes GraphRAG practical. Without it, the system has no way to connect a user's natural language question to the structured data in the graph. Entity linking can be done using named entity recognition models, which identify entity mentions in text, followed by a disambiguation step that matches each mention to the correct node in the graph.

Fraud Detection

Financial institutions use knowledge graphs to detect fraud rings. Accounts, transactions, devices, phone numbers, and addresses are nodes. Edges connect accounts that share a device, transferred money to each other, or used the same phone number. A fraud ring appears as a cluster of tightly connected nodes in the graph. Graph algorithms can detect these clusters automatically, flagging accounts that are part of a suspicious network even when no individual transaction looks fraudulent.

Supply Chain

Manufacturing companies use knowledge graphs to map their supply chains. Products, components, suppliers, and facilities are nodes. Edges represent supplies, manufactures, and ships to relationships. When a supplier experiences a disruption, the graph shows which products are affected and which alternative suppliers can fill the gap. This kind of analysis is difficult in a relational database because it requires traversing multiple levels of the supply chain, each involving joins across different tables.

Recommendation Engines

E-commerce platforms use knowledge graphs to power product recommendations. Users, products, and categories are nodes. Edges represent purchased, viewed, rated, and belongs to relationships. To recommend products to a user, the graph finds products that similar users purchased, products in the same category as previously purchased items, and products frequently co-purchased with items in the user's history. The graph structure makes these multi-hop queries efficient.

Enterprise Search and GraphRAG

Organisations use knowledge graphs to make internal documents searchable and to ground AI assistants in company data. Documents, people, projects, and departments are nodes. Edges connect documents to their authors, projects to their team members, and departments to their projects. When an employee asks an AI assistant about a project, the system traverses the graph to find related documents, team members, and dependencies, giving the language model structured context that text retrieval alone cannot provide.

Data Quality and Maintenance

A knowledge graph is only as accurate as the data it contains. Entities change over time. People change jobs, companies merge, products are discontinued. If the graph is not updated, it becomes stale and its answers degrade. Maintaining a knowledge graph requires ongoing data quality processes, including entity resolution for new data, deduplication, and validation of relationships. This is a continuous operational cost, not a one-time setup task.

Building Effort

Building a knowledge graph requires more upfront effort than setting up a vector database. You need to design a graph data model, choose an organising principle, build entity resolution pipelines, and implement ingestion logic. For a team that just needs to answer questions from a set of documents, standard RAG with a vector database is faster to build and simpler to maintain. A knowledge graph earns its investment when the use case involves complex relationships, multi-hop queries, or the need to integrate structured data from multiple sources.

Query Complexity

Querying a knowledge graph requires a different skill set than querying a vector database. Graph query languages like Cypher and SPARQL have their own syntax and semantics. Writing efficient graph queries requires understanding traversal patterns, index usage, and query optimisation. Teams without graph database experience will need training or external expertise to get productive.

Scalability

Graph databases scale differently from relational databases. As the graph grows, query performance depends on how well the traversal patterns align with the graph structure and indexes. Some queries that perform well on a small graph become slow on a large one because the traversal touches too many nodes. Sharding a graph across multiple servers is harder than sharding a relational table because relationships can cross server boundaries. Distributed graph databases exist, but they add complexity and have trade-offs in query performance.