What is a knowledge graph?
A knowledge graph is a way of storing information as a network of entities (people, products, locations, documents, events) connected by named relationships (supplies, owns, located in, approved by). Each fact is a triple: a subject, a relationship, and an object. “Acme Castings supplies part 4471” and “part 4471 is used in pump model P-200” are two triples, and following them answers a question that neither record answers alone: which products depend on Acme Castings. An ontology usually defines the allowed entity types and relationships, and the graph holds the instances.
Knowledge graphs are stored in graph databases or in triple stores and queried with languages designed to traverse relationships. Unlike relational tables, they handle highly connected, irregular data well and let new relationship types be added without redesigning the schema.
Why a knowledge graph matters
Many business questions are about connections rather than totals: which customers are exposed if a supplier fails, which contracts reference a regulation that just changed, and which employees have worked with a client and hold the certification a project needs. Answering those questions from separate ERP, CRM, and document systems means joining data that was never designed to join. A knowledge graph built on top of those systems makes the connections explicit once and keeps them current.
Knowledge graphs have also become central to generative AI. Retrieval-augmented generation over loose text chunks often misses answers that require combining facts from several documents. GraphRAG retrieves from a knowledge graph instead, giving the language model structured, connected facts and a path it can cite. The result is fewer hallucinations and answers that can be checked. In Kirk Tech Solutions data engagements, a graph layer is often added over a lakehouse when the questions users ask are about relationships rather than aggregates.
Knowledge graph in practice
An industrial distributor builds a graph connecting products, suppliers, substitutes, customer sites, installed equipment, and service history. When a supplier announces a discontinuation, the graph lists every affected SKU, every customer with that part installed, and the approved substitutes. Its customer assistant uses the same graph to answer “what filter fits the compressor at the Dayton plant” with a specific part number and the reason it is compatible.
What to watch for
- Start with a few high-value relationship types and the questions they answer, and grow the graph from there.
- Decide how the graph stays synchronized with the source systems, because a stale graph gives confident wrong answers.
- Apply access control at the entity and relationship level, so retrieval respects who may see what.
- Invest in entity resolution, since the same supplier spelled three ways produces three disconnected nodes.