  In this paper we introduce the notion of Demand-Weighted Completeness,
allowing estimation of the completeness of a knowledge base with respect to how
it is used. Defining an entity by its classes, we employ usage data to predict
the distribution over relations for that entity. For example, instances of
person in a knowledge base may require a birth date, name and nationality to be
considered complete. These predicted relation distributions enable detection of
important gaps in the knowledge base, and define the required facts for unseen
entities. Such characterisation of the knowledge base can also quantify how
usage and completeness change over time. We demonstrate a method to measure
Demand-Weighted Completeness, and show that a simple neural network model
performs well at this prediction task.
