matchingstap

Matchingstap is a term used in data integration and record linkage to describe a single step in an iterative matching pipeline that evaluates whether two records from different datasets refer to the same real-world entity. A matchingstap follows initial blocking and candidate generation and precedes later reconciliation or clustering steps.

The term combines 'matching' with stap, a word meaning step in Dutch, and is found in some Dutch- and international data science literature to denote a discrete stage in an entity-resolution workflow.

In a typical matchingstap, fields such as names, addresses, dates, and identifiers are prepared and compared. Attribute-level similarity scores (for example Levenshtein distance, Jaccard similarity, or numeric tolerances) are combined into a composite match score. Based on predefined thresholds or a trained classifier, the pair is labeled as a match, non-match, or possible match for further review.

Applications include deduplication of customer databases, linking records across merchant and service datasets, and privacy-preserving record linkage when combined with secure multi-party computation. The matchingstap is a design choice affecting recall and precision and is often tuned as part of a multi-stage pipeline.

Challenges include data quality, missing values, inconsistent formats, and the risk of biased or overconfident decisions. Performance depends on effective blocking, feature engineering, and calibration. Evaluation typically uses labeled data to measure precision, recall, and F1-score. Related concepts include entity resolution, record linkage, blocking, and similarity measures.