Blocking in Record Matching
Understand how a candidate-selection rule can miss known pairs and why missing fields need coverage checks before identity review.
A fictional postcode rule illustrates why absence from a comparison queue cannot establish that two hall records are different.
Blocking selects candidate pairs through shared features, narrowing comparisons without deciding identity. A postcode rule is one possible example.
Suppose a fictional hall roster compares names only within the same postcode. “North Hall” and “N Hall” enter the queue when both have that postcode. If one record lacks it, the pair may never be compared. That omission means the rule did not retrieve the pair, not that the halls are different.
Dedupe describes shared-feature comparison in its blocking explanation. For your workflow, record the actual rules and their versions. Test them against owner-confirmed matching pairs, including cases with missing or changed fields. Consider an alternative block when a useful field is absent, then inspect the additional candidates rather than accepting them automatically.
The duplicate-record review workflow preserves why each pair entered the review queue. That evidence helps distinguish candidate-coverage problems from mistaken match decisions.
For cross-source linkage, also keep each source’s namespace and grain. Shared attributes may be common across several locations or accounts. Passing a blocking rule earns a comparison, not an identity decision or permission to join the records.
Report missed known pairs separately from disputed identity decisions. The first suggests a retrieval rule needs review; the second needs source evidence and a named owner to resolve the relationship.
Filed under
entity-matching · blocking-record-matching
Related
- DedupeEvaluate Dedupe through an owner-reviewed matching pilot with known pairs, preserved source keys, candidate coverage checks and unresolved cases.
- Review AI Duplicate-Record Candidates Before MergingSeparate duplicate-record candidate generation from human match decisions, retaining source keys and field evidence before any merge.
- Link Records Across Datasets with AI-Assisted ReviewBuild a cross-source entity crosswalk that preserves both record keys, unresolved candidates and cardinality decisions without silently joining or merging data.