  We propose the novel task of answering regular expression queries (containing
disjunction ($\vee$) and Kleene plus ($+$) operators) over incomplete KBs. The
answer set of these queries potentially has a large number of entities, hence
previous works for single-hop queries in KBC that model a query as a point in
high-dimensional space are not as effective. In response, we develop RotatE-Box
-- a novel combination of RotatE and box embeddings. It can model more
relational inference patterns compared to existing embedding based models.
Furthermore, we define baseline approaches for embedding based KBC models to
handle regex operators. We demonstrate performance of RotatE-Box on two new
regex-query datasets introduced in this paper, including one where the queries
are harvested based on actual user query logs. We find that our final
RotatE-Box model significantly outperforms models based on just RotatE and just
box embeddings.
