A knowledge graph stores facts as connected entities, such as people, places and the relationships between them. Pairing one with a language model lets the model look up precise, verified facts instead of relying only on patterns learned from text, which reduces errors on questions with definite answers.
This combination suits areas like medicine, law and company data where accuracy matters and relationships are complex. The graph must be built and kept up to date, which takes effort, and connecting it smoothly to a model is an active area of work, but together they pair the fluency of language models with the dependability of curated facts.