entitetsmatching

Entitetsmatching, commonly referred to in English as entity matching, is the task of identifying and linking records that refer to the same real-world entity across different data sources. It is central to data integration, master data management, and knowledge graph construction, enabling a unified view of people, organizations, products, or events.

The process typically includes data preparation and cleansing, schema reconciliation, and methods to reduce computation such as blocking or indexing. Matching itself can be accomplished with deterministic rules (exact or keyed matches on selected fields), probabilistic record linkage, or machine learning classifiers trained on labeled pairs. Features often combine field-level similarities—such as exact matches, phonetic encodings, and string distances like Levenshtein or Jaro-Winkler—with domain-specific signals such as address normalization or customer identifiers.

Challenges in entitetsmatching include heterogeneity of schemas and data formats, multi-valued attributes, missing or noisy data, spelling variants, and privacy constraints. Scalability is addressed through blocking and indexing strategies, approximate methods, and distributed processing. Ambiguity in matches and transitive relations (if A matches B and B matches C, then A may match C) also complicate clustering outcomes and require careful decision rules or probabilistic models.

Applications span customer data integration, supplier and product matching, healthcare record linkage, fraud detection, and the consolidation of bibliographic or scholarly records. Evaluation typically uses precision, recall, and F1 scores, along with clustering-focused metrics when entities are grouped. Related concepts include deduplication, record linkage, data cleansing, master data management, and knowledge graph construction.