A Theory for Record Linkage.¶
Fellegi, I. P., & Sunter, A. B. (1969). A Theory for Record Linkage. Journal of the American Statistical Association, 64(328), 1183-1210.
Cited by¶
8 citations across 8 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Binding Problem
- Data engineering: joining records across sources where the same entity carries different identifiers — the entity-resolution problem — is the binding problem for tabular data, with foreign keys, surrogate keys, and probabilistic record linkage as binding mechanisms.
This sourceFoundational theory of probabilistic record linkage / entity resolution — binding records that refer to the same entity across sources lacking a shared key.
- Data engineering: joining records across sources where the same entity carries different identifiers — the entity-resolution problem — is the binding problem for tabular data, with foreign keys, surrogate keys, and probabilistic record linkage as binding mechanisms.
- Complexity
- Rather than treating it as straightforward technical merger, the team analyzes through multiple complexity lenses: (a) computational complexity—data-reconciliation algorithms (matching patient identities across systems with typos, missing fields, duplicates) are formally hard (record-linkage variant); they choose probabilistic matching with human review rather than exact algorithms
This sourceProbabilistic decision-theoretic model for matching records representing the same entity across files with noisy/incomplete data — supports the probabilistic-matching claim at D26-085. Verified existing and supporting. (See flag: prose asserts record linkage is 'NP-hard', which this statistical model does not establish.)
- Rather than treating it as straightforward technical merger, the team analyzes through multiple complexity lenses: (a) computational complexity—data-reconciliation algorithms (matching patient identities across systems with typos, missing fields, duplicates) are formally hard (record-linkage variant); they choose probabilistic matching with human review rather than exact algorithms
- Criteria of Individuation
- The co-reference criterion is the master-patient-index matching rule — deterministic on a national identifier where present, probabilistic on name, date of birth, and address where not — resolving which presentations are the same patient, with transitive closure as its equivalence regime (so one over-permissive pairwise match can cascade unrelated records into a phantom patient).
This sourceFoundational probabilistic record-linkage theory underlying entity resolution / master-data matching with its false-merge and false-split error trade-offs.
- The co-reference criterion is the master-patient-index matching rule — deterministic on a national identifier where present, probabilistic on name, date of birth, and address where not — resolving which presentations are the same patient, with transitive closure as its equivalence regime (so one over-permissive pairwise match can cascade unrelated records into a phantom patient).
- Equivalence Relation
- Data engineering develops the most operationally significant industrial application of equivalence-relation reasoning in the form of record linkage and master data management, drawing on the probabilistic linkage framework of Fellegi and Sunter (1969).
This sourceFounds probabilistic record linkage: assigns match/non-match probabilities to record pairs from similarity-feature vectors; the match decisions yield (after transitive closure) an equivalence relation whose classes are master entities.
- Data engineering develops the most operationally significant industrial application of equivalence-relation reasoning in the form of record linkage and master data management, drawing on the probabilistic linkage framework of Fellegi and Sunter (1969).
- Identity Test
- A quantitative test (do these records match on enough fields?) and a qualitative test (do these records refer to the same person regardless of data?) give different answers on the same dataset and pursue different goals — a tax authority and a marketing department deduplicate the same customer database with different tests and produce different counts, each correct for its purpose.
This sourceFoundational probabilistic record-linkage framework (developed for census deduplication) with explicit false-match/false-nonmatch rates — the tunable identity test later ported into epidemiology and master-patient-index design.
- A quantitative test (do these records match on enough fields?) and a qualitative test (do these records refer to the same person regardless of data?) give different answers on the same dataset and pursue different goals — a tax authority and a marketing department deduplicate the same customer database with different tests and produce different counts, each correct for its purpose.
Mechanisms¶
- Collision Detection Review
- This is the standard practice of record linkage, the discipline of deciding when two records describe one entity.
This sourceDefines record linkage as deciding whether a pair of records represents the same underlying person, object, or event.
- This is the standard practice of record linkage, the discipline of deciding when two records describe one entity.
- Deduplication Pass
- The problem it solves is a well-studied one; probabilistic record linkage gives it a principled basis
This sourceDevelops a probabilistic decision framework for the established problem of linking records that refer to the same entity.
- The problem it solves is a well-studied one; probabilistic record linkage gives it a principled basis
- Entity Resolution Policy
- Statistical record linkage from the Fellegi–Sunter model onward gives principled match probabilities, but a probability is an input to the rule, not a license to skip it.
This sourceDevelops a probabilistic record-linkage decision rule in which comparison-pattern probabilities are inputs to link, non-link, and possible-link classifications.
- Statistical record linkage from the Fellegi–Sunter model onward gives principled match probabilities, but a probability is an input to the rule, not a license to skip it.
Verification¶
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