  Knowledge graphs (KGs) are inherently incomplete because of incomplete world
knowledge and bias in what is the input to the KG. Additionally, world
knowledge constantly expands and evolves, making existing facts deprecated or
introducing new ones. However, we would still want to be able to answer queries
as if the graph were complete. In this chapter, we will give an overview of
several methods which have been proposed to answer queries in such a setting.
We will first provide an overview of the different query types which can be
supported by these methods and datasets typically used for evaluation, as well
as an insight into their limitations. Then, we give an overview of the
different approaches and describe them in terms of expressiveness, supported
graph types, and inference capabilities.
