A practical entity-normalization flow for clean knowledge graphs
Roger_M_Taylor · x · 2026-07-24
The image lays out a full entity-normalization workflow for knowledge graphs:
- Extraction pipeline: documents → chunking → chunks → LLM extraction → entities & relationships.
- Normalization: run exact match, fuzzy match, and semantic match for each entity.
- Decision point:
- if matched, reuse the canonical name;
- if not, keep the extracted name.
- Deduplication: embed entities, then do semantic search and fuzzy search.
- Threshold policy:
- >= 0.95: merge the node
- 0.85–0.95: flag for human review
- <= 0.85: create a new node
The author’s main point is that entity resolution and deduplication are different problems and should use different algorithms. A false split is tolerable; a false merge corrupts the graph.
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