Sameining

Sameining is a term used in information science to describe the process of identifying and applying a common representation to semantically related elements across heterogeneous data sources. The goal is to enable reliable data integration, retrieval, and reasoning by reducing semantic heterogeneity.

Concept and methods: It involves mapping equivalent concepts, terms, or attributes across sources to a shared ontology or schema. Techniques include schema matching, ontology alignment, and instance-based similarity measures. Practical sameining often combines automated similarity scoring with human curation. Data models, identifiers, and controlled vocabularies are used to encode the shared representations.

Applications: It is used in data warehousing, enterprise data integration, interoperability between organizations, and knowledge graphs. In practice, sameining supports unified search, cross-system analytics, and data governance.

Challenges: Ambiguity, polysemy, evolving schemas, and incomplete data can hinder accurate mappings. Overly aggressive mappings can obscure domain-specific nuance; transparent provenance and revision history are commonly recommended.

Example: A library database uses fields "Author," "Writer," and "Creator." Sameining would map those to a single concept "Author" within the shared schema, enabling unified queries across catalogs.

See also: schema matching, ontology alignment, data integration, knowledge graphs, data governance.