  Complex logical query answering (CLQA) in knowledge graphs (KGs) goes beyond
simple KG completion and aims at answering compositional queries comprised of
multiple projections and logical operations. Existing CLQA methods that learn
parameters bound to certain entity or relation vocabularies can only be applied
to the graph they are trained on which requires substantial training time
before being deployed on a new graph. Here we present UltraQuery, an inductive
reasoning model that can zero-shot answer logical queries on any KG. The core
idea of UltraQuery is to derive both projections and logical operations as
vocabulary-independent functions which generalize to new entities and relations
in any KG. With the projection operation initialized from a pre-trained
inductive KG reasoning model, UltraQuery can solve CLQA on any KG even if it is
only finetuned on a single dataset. Experimenting on 23 datasets, UltraQuery in
the zero-shot inference mode shows competitive or better query answering
performance than best available baselines and sets a new state of the art on 14
of them.
