Samematching

Samematching is a data-processing approach used in entity resolution to decide when two records refer to the same underlying real-world entity based on exact matches on a defined set of attributes. It relies on a strict equivalence relation, where records are considered the same only if the selected keys are identical after normalization. This contrasts with fuzzy, probabilistic, or learning-based matching, which allow partial similarity or uncertainty.

Key concepts in samematching include the choice of key attributes, normalization rules, and efficient grouping. Normalization may involve case folding, trimming whitespace, and removing punctuation to ensure that simple formatting differences do not prevent an exact match. A composite key is often created from multiple attributes, such as name, date of birth, and postal code, and records sharing that key are treated as matching.

An implementation typically follows these steps: normalize the chosen fields; select a set of key attributes; compute a composite key for each record; group records by that key using a hash or index; within each group, merge or link records to form a single entity representation; output the resulting clusters. Blocking strategies may be used to scale the process to large datasets.

Applications of samematching appear in data deduplication, customer data integration, bibliographic reconciliation, and inventory or asset matching where exact attribute equality suffices. Limitations include sensitivity to data quality and inconsistent encoding, the need for careful normalization, and the possibility of missing true matches when exact-key criteria are too strict. See also: exact matching, record linkage, entity resolution.