Abstract: 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 the 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 models significantly outperform models based on just Rotate and just box embeddings.