Entityperson

Entityperson is a term used in information science to describe the representation of a human individual as a data entity within information systems, databases, or knowledge graphs. The concept emphasizes treating a person both as an identity-bearing subject and as a node in a network of attributes, events, and relationships.

A typical entityperson model includes a unique identifier, core biographical attributes (such as name, date of birth, and nationality), and a set of relationships (family, employment, affiliations) as well as events and activities (transactions, interactions). It may also capture provenance and trust information, showing when and by whom data were created or updated. In practice, entityperson draws on established semantic models, including FOAF and schema.org Person, and is compatible with Linked Data principles.

Data governance concerns center on privacy, consent, accuracy, and security. Implementations must address identity resolution, de-duplication, access control, data minimization, and the right of individuals to access, correct, or delete their data. The term is most often discussed in the context of knowledge graphs and identity graphs where robust linking of disparate records is necessary.

Applications of the entityperson concept include customer relationship management, digital archives, social networks, and AI systems that reason about people and relationships. Critics highlight risks of overgeneralization, privacy harms, and unintended inferences if data about individuals is aggregated across systems without clear consent or oversight.

Related concepts include knowledge graphs, FOAF, schema.org Person, identity graphs, and data governance.