What Is Knowledge Graph?
Also known as: graph RAG, GraphRAG, enterprise knowledge graph
Definition
A knowledge graph stores things (parts, suppliers, lots, machines, documents) as nodes and their relationships as edges. In AI it lets a system answer multi-hop questions, such as which customers received parts from a bad lot, that plain text retrieval handles poorly.
Knowledge Graph Explained
Vector search finds passages that sound similar to the question. It struggles when the answer requires following relationships across records: this lot came from that supplier, was used in these work orders, shipped in those orders to these customers. A knowledge graph represents those links explicitly, so a query can traverse them reliably.
ERPs already hold most of the relationships in relational tables: bill of materials, routings, purchase orders, lot and serial transactions, ship-to records. A graph layer projects them into nodes and edges, for example item to component, job to lot, lot to supplier, in a graph database such as Neo4j or in graph queries over relational data. Documents and nonconformance reports can be linked to the same part and supplier nodes.
GraphRAG combines the two. The system identifies entities in the question, traverses the graph to collect connected facts, and adds those facts, along with relevant text passages, to the LLM's context. This is especially valuable for recall and traceability, impact analysis for engineering changes, and where-used questions across multilevel bills of material.
Building and maintaining a graph is real work. Entity matching across systems, keeping the graph in sync with the ERP and deciding which relationships matter all take effort. If your questions are mostly lookups and summaries, text retrieval or text-to-SQL is simpler and usually enough.
Why It Matters
- Supports multi-hop questions such as forward and backward lot traceability that plain RAG answers incompletely.
- Makes AI answers explainable, because the path through the graph can be shown.
- Unifies ERP records, quality documents and supplier data under shared entities.
- Adds build and sync cost, so it should be justified by relationship-heavy use cases.
In Practice
A supplier notifies you that steel lot 24-0817 failed a hardness check. A graph query starts at the lot node, follows edges to 6 work orders that consumed it, then to 3 serialised assemblies and the 2 customers they shipped to. The LLM turns the result into a containment summary listing affected serial numbers, ship dates and customer contacts, with the traversal path shown for the quality team to verify.
Frequently Asked Questions
Do I need a graph database?
Not always. Many relationship queries can run as recursive SQL over ERP tables. A graph database helps when paths are deep, varied and queried often.
How is a knowledge graph different from a vector database?
A vector database finds similar text. A knowledge graph stores explicit relationships and lets you traverse them. They are often used together.
Where does the graph data come from?
Mostly from ERP tables such as BOM, work order, lot transaction and purchase order, plus metadata extracted from documents, kept in sync on a schedule.
Is it worth it for a mid-size manufacturer?
If recall, traceability or change-impact questions are frequent and costly, yes. For simple Q&A over live data, text-to-SQL and RAG are simpler.
Related Terms
Traceability
Traceability is the ability to follow the history, location, and application of a part or material through the supply chain and production process, both backward to its sources and forward to every product and customer it reached.
RAG (Retrieval-Augmented Generation)
RAG (Retrieval-Augmented Generation) is an architecture that searches your own document store for passages relevant to a question, inserts them into the LLM prompt, and instructs the model to answer only from that retrieved evidence.
BOM Explosion
BOM explosion is the step in which MRP expands each parent item's planned quantity into dependent component requirements using the bill of materials, quantity-per factors, scrap and yield allowances, and effectivity dates, level by level.
Vector Database
A vector database is a system that stores high-dimensional embedding vectors and retrieves the ones most similar to a query vector, enabling semantic search where results match meaning rather than exact keywords.
Embedding
An embedding is a list of numbers, typically several hundred to a few thousand dimensions, that represents the meaning of a piece of text, image, or code so that semantically similar items produce vectors that sit close together.
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