What is a knowledge graph? Connecting entities, relations, and evidence
Understand knowledge graphs through products, vendors, and versions, including graph databases, embeddings, entity resolution, time qualifiers, and evidence.
Understand the concept
A knowledge graph organizes entities and relationships, such as products, vendors, and successor versions, with attributes and provenance. It is more than a visualization and does not guarantee truth. Graph databases are one storage option; vectors represent different similarity information. Resolve names and changes over time and trace queried relations to evidence.
Entity identity and relation meaning determine usefulness
Products sharing an abbreviation need separate IDs. Developer and reseller relationships differ; confusing them returns a sales channel for a developer question. More edges do not mean better knowledge, especially when entity merges corrupt paths.
Neo4j describes nodes, relationships, and properties, while Google’s API illustrates entity IDs and types. Implementations differ; entity search is not a full-graph download. No real vendor relations or graph database were built here.
Relations need provenance and validity conditions
Employment relations can expire, and version capabilities may depend on plans. Timeless edges can answer current questions incorrectly. Preserve source locations and time or scope qualifiers and handle superseded relationships rather than accumulating conflicts.
Extraction can misread negation or subjects; structuring does not remove errors. Check original sentences, direction, and conditions. A graph path is not proof of causation or mutual influence.
Keep graph and vector evidence distinct when combining them
Vectors can locate similar text and graphs query explicit relations. A product lookup can lead to version relations, still checked against sources. Graph use does not eliminate hallucination, and similarity should not become a confirmed factual edge.
Test homonyms and time-qualified questions for correct entities and relations. This is a test design without scores. For free-text questions, graph construction should depend on relational needs and maintenance, not terminology appeal.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.