Evaluate entity resolution tools on representative records from your own source systems, using known match outcomes wherever practical. Compare precision and recall, inspect both record-pair decisions and the resulting entity clusters, and find out which candidate pairs the tool never considered. If you lack complete, representative labels, treat quality estimates as estimates—not ground truth. No universal best tool or comparable current vendor price ranking is established by the available evidence.
What should an entity resolution evaluation establish?
Entity resolution—also called record linkage, data matching, or duplicate detection—determines whether records refer to the same real-world entity, within one dataset or across multiple datasets. A useful evaluation answers two questions: how often does the tool make the right links, and what are the consequences when it gets them wrong?
Those consequences depend on what happens downstream. A false merge joins records that refer to different entities; a missed merge leaves records for the same entity disconnected. The cost of each error can differ by use case, so set acceptance criteria with the data owner and the people responsible for decisions based on the resolved records. There is no universal threshold to borrow from a vendor default.
How do you build a fair test?
Define the entity and the decision
Before comparing products, record what counts as one entity, whether the task is within-source deduplication or cross-source linkage, and how the output will be used. Specify how reviewers should label a pair as a match or non-match, including how to handle ambiguous cases. Keep the rules consistent across tools.
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Use records that reflect production
Build a holdout sample from the actual source mix. It should include the missing fields, formatting differences, and difficult records likely to appear in production—not just clean, easy examples. Where practical, have knowledgeable reviewers adjudicate match and non-match labels, and document who labeled them and what rules they followed.
Use the same sample and labels for every shortlisted tool. If labels cover only some records or systematically omit difficult cases, disclose that limitation: measured performance may not describe the full workload.
Which metrics should you report?
Precision and recall, with counts
For labeled record pairs, report precision and recall rather than relying on accuracy alone:
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- Precision is the share of pairs the tool predicted as matches that are true matches. Low precision means more false links.
- Recall is the share of true-match pairs that the tool found. Low recall means more missed links.
Include the underlying counts or denominators so readers can see how many false links and missed links produced each score. A score without its counts can hide the size of the evaluation or the practical number of errors.
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Use a combined score only as a supplement
F-measure is the harmonic mean of precision and recall. It can summarize their tradeoff, but it should not replace the separate scores and counts: two tools with similar combined scores can have different false-link and missed-link rates. Decide which error is more consequential for the use case before treating a combined score as a decision rule.
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How do you assess clusters, not just pairs?
Some tools produce groups of records representing entities. Pair-level scores do not show the full effect of errors on those groups. A false link can bridge records from distinct entities into one incorrect cluster; missed links can leave one entity split across multiple clusters.
Inspect the resulting clusters and quantify incorrect merges and splits where your labels permit. Also examine how errors vary across sources, match-score bands, blocking patterns, and categories relevant to downstream analysis, where legally and operationally appropriate. UK linkage quality guidance calls for assessing false and missed links, clustering effects, and variation in errors across variables relevant to the analysis.
What happens before the final match decision?
Entity resolution is a multistage process. Candidate generation narrows the pairs a tool compares; a comparison method evaluates those candidates; a decision rule or model then accepts, rejects, or flags pairs. A strong score on pairs that were considered can conceal true matches that candidate generation excluded.
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Ask for candidate-generation visibility
Find out which pairs the system considered and which it never compared. Blocking can reduce the comparison workload, but exclusions can reduce recall. Evaluate candidate-generation behavior as part of the test rather than attributing every missed match to the final decision threshold.
Request decision evidence and review controls
Ask the vendor to show field-level comparisons, the rule or model path, match score, decision threshold, and the reason a record was sent for manual review. ONS describes a candidate-links table that records how each pair compares across attributes and notes that errors can enter at different pipeline stages. Explanations like these help reviewers locate whether a failure came from candidate generation, attribute comparison, or the final decision.
How should you compare shortlisted tools?
Run each candidate against the same representative records, labels, entity definition, and acceptance criteria. Compare the following dimensions rather than reducing the decision to one quality score:
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| Evaluation axis | What to compare | Why it matters |
|---|---|---|
| Pair-level quality | Precision, recall, false links, missed links, and optionally F-measure | Shows the tradeoff between incorrect links and missed true matches. |
| Cluster quality | Incorrectly merged groups, split entities, and downstream effects | Pair-level measures alone may not reveal how errors affect grouped output. |
| Candidate generation | Candidate recall, blocking behavior, and which pairs were not considered | A true match cannot be linked if the candidate stage never compares it. |
| Robustness | Results by source, missingness, formatting variation, and relevant analysis categories | An overall average can hide weak performance on an important part of the data. |
| Reviewability | Field comparisons, decision reasons, thresholds, uncertain cases, and correction workflow | Supports audit, investigation, and resolution of errors. |
| Operating fit | Scale, integration, governance, data handling, deployment constraints, review effort, and workload-specific cost | A tool must fit the actual operating environment as well as meet quality criteria. |
Do not infer a general winner from the available published material: it does not establish independent, apples-to-apples vendor performance or workload-specific pricing. Cost and fit need to be assessed for your workload, including the effort required to review uncertain cases.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What changes when you have multiple sources?
Test the actual source mix rather than assuming a workflow that works for one source will behave the same across several. AWS documents a specific example: its default waterfall approach excludes records matched at a higher rule level from later rules. AWS says this may work well for single-source matching but can cause problems with multiple sources that have different attributes. Combining the logic into one overly permissive rule can risk overmatching.
AWS also documents transitive matching, which processes records across rule levels so records can connect later unmatched records to existing groups. These are descriptions of AWS product behavior, not independent performance results. Reproduce the relevant source mix in a trial and inspect both the links and resulting clusters before relying on either behavior.
What if you do not have ground-truth labels?
Without known match outcomes, you cannot report measured precision and recall as if they were ground truth. Say which records or outcomes are unlabeled, how that limits the evaluation, and whether any reported figures are estimates. Do not describe an estimated result as a verified error rate.
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A 2025 ACM paper, “Unsupervised Evaluation of Entity Resolution,” proposes methods for estimating precision, recall, and F-measure without ground truth and validates them on multiple datasets. Such methods can inform an evaluation when labels are unavailable, but their estimates are not a substitute for representative adjudicated outcomes. The paper does not establish the performance of any particular commercial tool.
Quick Recap
Which resources can help?
- AWS Entity Resolution documentation: consult the official user guide for current supported workflows and product-specific behavior. The documentation is vendor material, not an independent comparison.
- ER-Evaluation: this software package has a user guide for evaluating entity-resolution systems, record linkage, and deduplication. Confirm the current package version and that it suits your project before adopting it.
- “Unsupervised Evaluation of Entity Resolution” (2025): methodological research on estimating quality without ground truth.
- The 2024 arXiv preprint on entity-centric evaluation: proposes evaluating pairwise and cluster-level quality and analyzing errors. As a preprint, it should be treated as research rather than proof of a vendor’s performance.
A practical evaluation sequence
- Write the evaluation brief: define the entity, source scope, downstream use, harmful errors, and label rules.
- Prepare a representative holdout: include the real source mix and the messy cases expected in production; document label coverage and limitations.
- Run each shortlisted tool consistently: use the same records, labels, definitions, and acceptance criteria.
- Report pair-level results: provide precision, recall, false-link and missed-link counts, and F-measure only as a supplement.
- Inspect clusters and subgroups: examine merges, splits, and error variation across sources and analysis-relevant categories.
- Trace pipeline behavior: review excluded candidates, field comparisons, thresholds, decision explanations, and manual-review handling.
- Assess operational fit: compare review effort, throughput, integration, governance, deployment, data handling, and the cost of the workload you actually tested.
- Set acceptance criteria with decision owners: base them on the consequences of errors in your use case, not on an unexplained default or an unsupported industry-wide threshold.
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