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Research Abstract
This report studies GraphRAG and multimodal knowledge graphs by connecting Microsoft's article on discovery in private narrative data with Meituan's article on ingredient extraction and dish graphs, supplemented by later papers, datasets, and official documentation. It treats construction, evidence retrieval, and generation as distinct but interdependent stages and compares community summaries, associative memory, chunk organization, textual graph understanding, and multimodal retrieval. The objective is to identify conditions under which relationships and visual evidence provide verifiable value rather than select one universally optimal architecture.
The synthesis yields four judgments. Graph structure organizes cross passage connections, but value depends on identity, relation types, and tasks. Modalities complement observations without turning similarity into fact. Provenance, versions, permissions, and uncertainty must span construction through generation; adding citations afterward is insufficient. Adoption requires task benefits, failure analysis, and sustainable maintenance together. Outputs include an evidence architecture, layered evaluation, enterprise and teaching blueprints, a campus dish design, and a reproducible miniature experiment. The experiment verifies deterministic queries and arithmetic rather than large model performance. Chinese and English sections are paired, and figures distinguish source data, original designs, and assumed scenarios.