linkagesuch

Linkagesuch is a term used to describe a family of methods for discovering and analyzing linkages between entities across heterogeneous data sources. It emphasizes identifying direct connections and often uncovering indirect associations that emerge when datasets are joined or aligned.

Etymology: The name combines 'linkage' with the German word suche, meaning 'search.' The term is not widely standardized and appears mainly in experimental literature and practice notes as a label for cross-dataset linkage discovery tasks.

Core concepts and methods: Linkagesuch combines record linkage, entity resolution, and link mining. Common techniques include graph-based models that represent data as nodes and edges, probabilistic linkage to estimate the likelihood of connections, embedding-based similarity measures, and constraint-driven rule systems to guide valid linkages.

Applications: It has been proposed for integrating data in social networks, public health surveillance, supply-chain analytics, and market research, where discoveries about how entities relate across sources can inform interpretation and decision making.

Challenges: Data quality, privacy and consent, scalability to large datasets, and the lack of standardized evaluation metrics are major barriers. Interpretability of discovered linkages and the risk of spurious connections are also concerns.

See also: Record linkage, entity resolution, data fusion, network science, heterogeneous information networks.