pointsknown

Pointsknown is a term used in information science and knowledge representation to describe a metric that captures how well a data point or entity is documented and verifiable within a dataset or knowledge graph.

Definition: It denotes the degree to which an entity’s attributes and relations are supported by independent sources or clear provenance. It is often expressed as a score from low to high, reflecting verifiability, completeness, and internal consistency.

Variants: Operationalizations vary. Some use a binary known/unknown flag; others use a continuous score that combines source trust, attribute coverage, and data freshness.

Applications: Pointsknown is used in data curation, entity resolution, data quality assessment, and knowledge-graph maintenance. It helps prioritize data points for verification, enrichment, or conflict resolution.

Computation and limitations: In practice, it may weigh sources by trust, count supported attributes, and penalize contradictions. There is no universal standard, and subjectivity in source trust or data model heterogeneity pose challenges.

History and status: The term is not part of a formal standard and appears mainly in theoretical discussions and exploratory studies as a placeholder for data verifiability.

See also and example: See also data provenance, data quality, verifiability, confidence score, and knowledge graph reliability. In a bibliographic knowledge graph, a publication node with multiple independent citations and verifiable metadata would have a higher pointsknown than a node with few sources.