Knowledge Graph
A knowledge graph is a database of things and relationships: entities (people, parts, sites, contracts) are nodes, typed relationships are edges, and questions are answered by following paths through the graph.
No jargon, no math
In plain words, what is it?
Instead of searching text, the graph knows facts. Building it means mining documents for entities (suppliers, parts, plants, customers) and relations with a precise type: supplies, fits, approves, located in. Every edge carries provenance: which document it came from. A question becomes a path to walk: “which suppliers cover the parts that line 3 depends on?” resolves by walking line to machine, machine to part, part to supplier. The advantage over vector search is precision: the graph reasons in exact identities, not resemblance. The limits: building it is real work, messy text yields messy relations, and facts change while the graph ages, so in production it is often paired with retrieval (GraphRAG).
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Extraction. Documents are mined for entities: parts, suppliers, sites, people.
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Typing. Relations get precise labels: supplies, fits, approves, located in.
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Storage. Nodes and edges land in the graph, every edge traceable to its source.
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Query. A question becomes a path to walk through the graph.
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Answer. The response comes with the exact chain of relationships it used.
Terms that live nearby.
Semantic Search
Semantic search finds content by meaning instead of exact words: the query and the documents are converted into embeddings, and the closest matches are returned even when no keyword overlaps.
ReadUnstructured Data
Unstructured data is information with no fixed schema: documents, emails, images, audio, chat logs, PDFs. Most of what a company knows lives in this form, but software must first parse, transcribe or embed it before it can be searched or counted.
ReadData Pipeline
A data pipeline is the connected chain of steps that carries data from where it is produced to where it is consumed: ingest, validate, transform, store and serve, each stage handing clean, well-formed data to the next.
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