Data Graphs with Incomplete Information (and a Way to Complete Them)

We introduce a modal language for reasoning about data graphs with incomplete information. Such data graphs are formally represented as models in which data value functions are partial-to capture what is unknown. In this setting, we also allow for unknown data values to be learned. Our main result is a sound and strongly complete axiomatization for the logic.