Capsmatch

Capsmatch is a framework for capitalization-aware data matching and entity resolution. It aims to improve linkages between records that refer to the same real-world entity when data contain inconsistent or unusual capitalization.

The approach treats capitalization patterns as informative signals rather than noise. It uses normalization that preserves meaningful case features, extracts features such as capitalized token sequences, title-case patterns, and all-caps tokens, and combines them with standard string similarity measures. Capsmatch often integrates case-sensitive and case-insensitive comparisons to capture both identity cues and orthographic variants.

A typical workflow includes data ingestion, case-aware normalization, tokenization, feature extraction, candidate generation, and scoring to produce a match probability. Capsmatch often employs a hybrid scoring model that blends rule-based checks with machine-learned ranking to handle ambiguities and scale to large datasets. It can be used in conjunction with existing deduplication and record-linkage frameworks to improve precision without sacrificing recall.

Applications span customer data integration, bibliographic databases, supplier or partner records, contact deduplication, and multilingual corpora where capitalization conventions vary. The framework is commonly implemented as libraries or services in popular programming environments and can be integrated into ETL pipelines and data-cleaning workflows.

Limitations include language-specific capitalization rules and the potential for bias toward records with richer capitalization signals. Ongoing work focuses on cross-language capitalization patterns, disambiguation in noisy data, and better integration with contextual information. See also entity resolution, record linkage, and fuzzy matching.