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01-22-2026 02:07 PM
Hey. We’ve run into similar deduplication problems before. If the name differences are pretty minor (punctuation, spacing, small typos), fuzzy string matching can usually get you most of the way there. That kind of similarity-based clustering works fine for straightforward cases.
Once names start to vary more though (abbreviations, reordered components, nicknames, or spellings that don’t look alike character-wise), fuzzy matching starts to fall apart because it’s only comparing characters, not meaning. That’s where semantic understanding helps.
In practice, fuzzy matching missed things like “A. Butoi” vs “Alexandra Butoi”, while a semantic approach did much better overall. You can read more in the FutureSearch case study here: https://futuresearch.ai/researcher-dedupe-case-study/
