Knowledge bases (KBs) provide interesting, yet incomplete samples from reality, and therefore generally do not properly represent it (e.g., Wikidata contains many more professors than janitors). These notability-based biases are somewhat systematic, however, and therefore raise the question of whether one can reverse-engineer (de-bias) parts of reality from such KBs.
The thesis shall develop and formalize a parametric process how data about reality enters KBs, and then use stochastic optimization to estimate the parameters of this process. All this should be evaluated on selected subsets of reality.