  Representation learning for knowledge graphs (KGs) has focused on the problem
of answering simple link prediction queries. In this work we address the more
ambitious challenge of predicting the answers of conjunctive queries with
multiple missing entities. We propose Bi-Directional Query Embedding (BIQE), a
method that embeds conjunctive queries with models based on bi-directional
attention mechanisms. Contrary to prior work, bidirectional self-attention can
capture interactions among all the elements of a query graph. We introduce a
new dataset for predicting the answer of conjunctive query and conduct
experiments that show BIQE significantly outperforming state of the art
baselines.
