What is a knowledge graph?
A knowledge graph is a structured representation of real-world entities and the relationships between them, stored in a format that AI systems and analytical tools can query and reason over. Entities — which might be products, assets, people, organizations, financial instruments, or any other concept — are represented as nodes. The relationships between them are represented as edges, with properties that describe the nature and attributes of each relationship.
The term “graph” refers to the mathematical structure: a network of nodes and edges in which any node can connect to any other node, creating a web of interconnected knowledge rather than the hierarchical tables of a relational database. This graph structure is what makes knowledge graphs powerful for AI: relationships between entities can be traversed, reasoned over, and used to answer complex questions that relational databases cannot handle efficiently.
How knowledge graphs work
A knowledge graph begins with an ontology: a formal definition of the entity types and relationship types that exist in the domain being modeled. The ontology is the schema — it defines what kinds of things the graph can represent and what kinds of connections are meaningful. For an industrial plant, the ontology might define entity types including equipment, components, sensors, maintenance tasks, and failure modes, and relationship types including “part of,” “monitored by,” “associated with,” and “caused by.”
Data from source systems is then mapped to this ontology: equipment records from maintenance systems, sensor identifiers from historian systems, maintenance histories from work order systems, and technical specifications from engineering documentation. This mapping process — which is what AI platforms with built-in knowledge graphs automate — transforms disconnected data from multiple source systems into a unified, queryable representation of how the domain is structured.
At query time, a knowledge graph can answer questions that require traversing multiple relationships: “show me all sensors on this equipment that have been associated with unplanned failures in the last 12 months,” or “which products in this category have promotional mechanics similar to the top-performing SKUs from last quarter.” These multi-hop queries, which would require complex joins across multiple tables in a relational database, are natural operations in a graph structure.
Knowledge graphs and AI
Knowledge graphs are a foundational component of enterprise AI systems because they provide the structured domain knowledge that AI models need to reason accurately and contextually.
A generative AI model connected to a knowledge graph can answer domain-specific questions with precision, because the graph provides verified, current, organization-specific facts rather than relying on the model’s training data. An operator asking “what is the maintenance history for this pump?” receives an answer drawn from the actual maintenance records in the graph, not a hallucinated approximation.
A predictive AI model enriched with knowledge graph features can incorporate contextual information about entities — the asset class, the maintenance regime, the operating environment — that raw sensor data does not capture. This additional context improves prediction accuracy, particularly for anomaly detection and failure prediction tasks where the relationship between an asset’s characteristics and its failure modes is important.
AI agents use knowledge graphs as a memory and reasoning substrate. Rather than maintaining context only within the current conversation, an agent with access to a knowledge graph can retrieve and reason over persistent organizational knowledge: what happened with this customer account, what is known about this supplier, what maintenance has been performed on this asset. The knowledge graph gives the agent durable, structured memory that persists across interactions.
Domain knowledge graphs
A domain knowledge graph is a knowledge graph built specifically for a defined industry or operational domain. Rather than a general-purpose knowledge base, a domain knowledge graph encodes the specific entity types, relationship types, and ontological structure of the target domain.
Industrial domain knowledge graphs encode the structure of manufacturing and process operations: the hierarchy of facilities, systems, equipment, components, and sensors; the relationships between assets and their failure modes; the connections between process steps and quality outcomes; and the organizational structures of maintenance and operations teams.
Financial domain knowledge graphs encode the relationships between legal entities, financial instruments, transactions, beneficial owners, and regulatory classifications. A financial crime knowledge graph maps the connections between accounts, transactions, counterparties, and known risk indicators — enabling AML analysts and AI systems to identify suspicious networks and relationships that would be invisible in flat transaction data.
Retail domain knowledge graphs encode the relationships between products, categories, suppliers, stores, promotions, and customer segments — providing the contextual structure that demand forecasting and assortment optimization models need to understand the difference between products, not just treat them as anonymous SKUs.
SymphonyAI builds domain knowledge graphs into its vertical AI platforms. The IRIS Foundry knowledge graph provides the industrial domain structure that underpins SymphonyAI’s industrial AI applications. The financial services platform uses relationship graphs to support financial crime detection and investigation workflows. These pre-built domain knowledge graphs are a key reason SymphonyAI’s vertical AI deploys faster than general-purpose AI: the domain structure does not need to be built from scratch by the customer.
Knowledge graphs vs. relational databases vs. vector databases
Relational databases store data in tables with fixed schemas. They are highly efficient for structured queries on well-defined data types but require complex joins to express multi-hop relationships and are difficult to adapt as the data model evolves. Knowledge graphs are more flexible and better suited to representing complex, variable relationship structures.
Vector databases store data as high-dimensional numerical vectors, enabling similarity search: finding items that are semantically similar to a query. Vector databases are used in AI applications for finding similar documents, similar products, or similar entities based on learned embeddings. Knowledge graphs and vector databases are complementary: knowledge graphs provide structured relational reasoning, vector databases provide semantic similarity search. Enterprise AI systems often use both.
Knowledge graphs are optimized for traversal and reasoning over entity relationships. They excel at multi-hop queries, pattern matching across networks of connected entities, and incorporating structured domain knowledge into AI reasoning. They are the right choice when the relationships between entities — not just the attributes of individual entities — are important for answering questions or making decisions.
Knowledge graphs in enterprise AI applications
Knowledge graphs are deployed across industries as the structured knowledge foundation for enterprise AI.
In industrial operations, the IRIS Foundry knowledge graph from SymphonyAI provides a unified representation of the industrial plant: all assets, their components, sensors, maintenance histories, operating parameters, and failure modes, connected in a structure that AI models and natural language interfaces can query. The knowledge graph enables SymphonyAI’s industrial AI copilot to answer questions about any asset in the plant from a natural language interface, because the graph provides the contextual knowledge the copilot needs to give accurate, specific answers.
In financial services, entity relationship graphs support financial crime investigation by mapping the connections between accounts, transactions, counterparties, beneficial owners, and known risk indicators. Investigators can traverse the graph to identify hidden relationships and networks that flat transaction screening would miss.
In retail and CPG, product and customer knowledge graphs provide the contextual structure for assortment optimization and demand forecasting models — ensuring that AI recommendations account for the relationships between products, the substitution dynamics between categories, and the segmentation of customers by behavior and demographics.
Frequently asked questions
What is a knowledge graph in simple terms?
A knowledge graph is a structured map of entities and the relationships between them. Think of it as a network where each node is a thing (a product, a piece of equipment, a person, an organization) and each connection between nodes is a relationship (this component is part of that machine, this transaction is linked to that account, this product belongs to that category). AI systems and analysts can traverse and query this network to answer complex questions that flat databases cannot handle efficiently.
What is the difference between a knowledge graph and a database?
A relational database stores data in structured tables and uses joins to express relationships. Knowledge graphs store data as networks of nodes and edges, making it natural to traverse chains of relationships (multi-hop queries) and to add new types of entities and relationships without restructuring the entire data model. Knowledge graphs are more flexible and better suited to representing complex, evolving relationship structures than relational databases.
How are knowledge graphs used in AI?
Knowledge graphs provide AI systems with structured, verified domain knowledge that improves accuracy, supports contextual reasoning, and reduces hallucination. Generative AI models connected to knowledge graphs can answer domain-specific questions grounded in current organizational facts. Predictive models enriched with knowledge graph features incorporate contextual information about entities that improves prediction accuracy. AI agents use knowledge graphs as persistent memory, enabling reasoning over organizational knowledge across multiple interactions.
What is a domain knowledge graph?
A domain knowledge graph is a knowledge graph built specifically for a defined industry or operational domain, encoding the entity types, relationship types, and ontological structure specific to that domain. Industrial domain knowledge graphs encode the structure of manufacturing operations — assets, components, sensors, failure modes. Financial domain knowledge graphs encode entity relationships relevant to financial crime detection. Domain knowledge graphs are a key component of vertical AI platforms because they provide the structured domain knowledge that makes AI accurate and contextually relevant in specialized enterprise settings.
What is the relationship between a knowledge graph and an ontology?
An ontology is the schema of a knowledge graph: the formal definition of what entity types and relationship types the graph can represent, and the rules governing how they connect. The ontology defines the structure; the knowledge graph is the populated instance — actual entities and actual relationships mapped according to that structure. An industrial ontology defines entity types like “rotating equipment” and “sensor”; the knowledge graph contains the actual pumps, valves, and pressure sensors of a specific plant, connected according to the ontology’s relationship rules.
Last updated: May 2026
