attributeuncertainty
Attributeuncertainty refers to the uncertainty associated with the values of attributes assigned to entities in data systems, knowledge representations, and statistical models. It captures the idea that an attribute's true value may be uncertain due to measurement error, missing data, or conflicting information. The concept is used across data quality, machine learning, information retrieval, and knowledge graphs to support principled reasoning under incomplete information.
Common sources include measurement noise, data entry mistakes, ambiguous attribute definitions, temporal changes, and conflicts among
Methods to model attributeuncertainty include probabilistic modeling and Bayesian inference, imputation for missing values, and uncertainty-aware
Applications include data cleaning and fusion, risk assessment, and robust decision making. Evaluation uses metrics from