  Formulating and answering logical queries is a standard communication
interface for knowledge graphs (KGs). Alleviating the notorious incompleteness
of real-world KGs, neural methods achieved impressive results in link
prediction and complex query answering tasks by learning representations of
entities, relations, and queries. Still, most existing query answering methods
rely on transductive entity embeddings and cannot generalize to KGs containing
new entities without retraining the entity embeddings. In this work, we study
the inductive query answering task where inference is performed on a graph
containing new entities with queries over both seen and unseen entities. To
this end, we devise two mechanisms leveraging inductive node and relational
structure representations powered by graph neural networks (GNNs).
Experimentally, we show that inductive models are able to perform logical
reasoning at inference time over unseen nodes generalizing to graphs up to 500%
larger than training ones. Exploring the efficiency--effectiveness trade-off,
we find the inductive relational structure representation method generally
achieves higher performance, while the inductive node representation method is
able to answer complex queries in the inference-only regime without any
training on queries and scales to graphs of millions of nodes. Code is
available at https://github.com/DeepGraphLearning/InductiveQE.
