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What record linkage is—and what “exact” means
Record linkage asks whether two records refer to the same real-world entity, such as a person, company, place, or product. Exact agreement on selected fields is one possible rule for answering that question; it is not a universal definition of identity.
An exact rule links records when specified values match, sometimes after a documented normalization step such as standardizing case or punctuation. Exactness therefore depends on which fields are selected, how they are normalized, and what the rule requires. A deterministic method applies predetermined rules, which may require exact equality on one or more attributes.
Probabilistic linkage weighs evidence across fields, allowing some fields to disagree when other evidence supports a match. “Fuzzy matching” is a broad term for approximate comparisons such as edit distance, phonetic similarity, or other similarity scores. These approaches are related, but fuzzy matching is not the same thing as semantic similarity.
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How the approaches compare
| Approach | Useful when | Main advantage | Main risk |
|---|---|---|---|
| Exact or deterministic matching | Selected identifiers are accurate, stable, consistently represented, and sufficiently specific. | Rules are straightforward to explain and audit; deterministic matching can be computationally fast. | Legitimate variation or missing values can leave true matches unlinked; shared or incorrect identifiers can link different entities. |
| Probabilistic or fuzzy matching | Records may vary in spelling, formatting, or completeness, and evidence is available across multiple fields. | It can use graded agreement rather than requiring every selected value to be identical. | Scores and thresholds can produce false links as well as missed links; results depend on data quality and validation. |
| Semantic similarity | Descriptive text, aliases, abbreviations, or paraphrases may refer to the same entity despite weak word overlap. | It can help retrieve plausible candidates or contribute a meaning-based signal. | Similar descriptions can refer to different entities, while different descriptions can refer to the same one. Similarity alone does not establish identity. |
When exact matching is the right choice
Prefer exact rules when identifiers are dependable for the population and purpose—for example, a verified unique identifier, or a validated combination of stable fields. Exact matching is especially useful when an identical value is a defensible link rule and you need an explainable, repeatable decision.
Do not assume an exact-only result includes every true match. Incomplete, stale, or differently recorded fields can cause records to be missed. UK Government privacy-preserving linkage guidance also warns that exact matching information can produce a non-randomly selected subset. Describe unmatched records as unmatched under the rule, not necessarily as different entities, and consider who is more likely to be excluded.
When probabilistic or fuzzy matching is a better fit
Use approximate comparisons when legitimate variation is expected, such as spelling differences, transposed characters, alternate forms, or imperfect identifiers. Where possible, combine evidence from multiple fields rather than relying on one imperfect value. Some matching systems let a rule combine exact and fuzzy conditions; AWS documents exact, cosine, Levenshtein, and Soundex functions as configurable components in its own service.
A score threshold determines which candidates are accepted, rejected, or referred for review. A higher threshold generally favors precision—the share of assigned links that are true—while a more permissive threshold may recover more true matches at the cost of more false links. There is no threshold that removes this trade-off for uncertain pairs.
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- If the task is broad case-finding and candidates will be checked later, greater recall—the share of true matches recovered—may be worth a larger review queue.
- Do not assume a more complex model is safer. Identifier quality and completeness affect errors regardless of the algorithm.
Where semantic matching helps—and where it stops
Semantic methods compare meaning or context, often using vector representations of text. They can help surface candidate pairs when descriptions use different words, but identity linkage asks a narrower question: whether the records denote the same entity. Two organizations may have similar descriptions but be different businesses; one organization may be described in quite different terms in separate records.
Use semantic similarity as a candidate-generation aid or one feature in a broader resolver. Combine it with identity-relevant evidence such as authoritative identifiers and field-specific comparisons, and validate the final decisions against the linkage task. Google’s embedding documentation states that vectors from gemini-embedding-001 and gemini-embedding-2 cannot be directly compared because their embedding spaces are incompatible. This is a vendor-specific version warning, not a rule about every embedding system. For reproducibility, retain the embedding model version and similarity procedure, and recalibrate or re-embed when those change.
A practical way to choose and implement a method
- Define what counts as the same entity. Specify the entity, population, fields, normalization, and downstream use. Agreement on a field only supports identity to the extent that the field is reliable and discriminative for this task.
- Assess field quality and coverage. Record missing, invalid, stale, or low-quality values. Check whether data quality differs across groups; a matching process can perform unevenly when identifier availability does.
- Set the error priorities. Decide what a false link and a missed link would cost in the downstream decision. Use those costs to set thresholds and determine which cases need human review.
- Build a representative reference set. Where feasible, have reviewers label a sample of candidate pairs as matches or non-matches. Use it to compare precision and recall for the actual data and intended use, not a generic performance claim.
- Inspect grouped results, if you create clusters. Pair-level scores can hide whether a false edge merged large groups or missed edges split one entity into several clusters. Assess both merging and splitting.
- Retain evidence for audit and downstream use. Preserve uncertain candidates and their scores or agreement patterns where possible, along with process details, field-quality information, and aggregate error measures.
Use blocking carefully
Blocking or indexing limits detailed comparisons to a smaller set of candidate pairs, reducing computation. It also determines which pairs ever reach the scoring stage: a true match excluded during candidate generation cannot be recovered by a later fuzzy or semantic score.
Measure linkage quality by blocking condition as well as overall. A staged design can apply high-confidence exact rules first, score remaining candidates with probabilistic or fuzzy methods, and send uncertain or high-impact pairs to review. Treat this as an option to evaluate, not a guaranteed improvement. ONS describes deterministic linkage as a possible first pass before probabilistic matching.
Best Value
If pairwise links are turned into groups through transitive matching, inspect how links combine across rules. AWS documents service-specific transitive matching behavior and warns that poor rule ordering can incorrectly group records that differ on unique fields; the details depend on that service and are not universal requirements for record linkage.
What a sound result should tell users
Report the method and the limits of the resulting links, not just a single accuracy figure. UK Government guidance recommends communicating process details, field and link quality, and aggregate information about errors, and providing uncertain links where possible. There is no universal performance percentage for exact, fuzzy, or semantic linkage: results must be measured for the records and purpose at hand.
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