  The existing literature on knowledge graph completion mostly focuses on the
link prediction task. However, knowledge graphs have an additional
incompleteness problem: their nodes possess numerical attributes, whose values
are often missing. Our approach, denoted as MrAP, imputes the values of missing
attributes by propagating information across the multi-relational structure of
a knowledge graph. It employs regression functions for predicting one node
attribute from another depending on the relationship between the nodes and the
type of the attributes. The propagation mechanism operates iteratively in a
message passing scheme that collects the predictions at every iteration and
updates the value of the node attributes. Experiments over two benchmark
datasets show the effectiveness of our approach.
