  Querying knowledge graphs (KGs) using deep learning approaches can naturally
leverage the reasoning and generalization ability to learn to infer better
answers. Traditional neural complex query answering (CQA) approaches mostly
work on entity-centric KGs. However, in the real world, we also need to make
logical inferences about events, states, and activities (i.e., eventualities or
situations) to push learning systems from System I to System II, as proposed by
Yoshua Bengio. Querying logically from an EVentuality-centric KG (EVKG) can
naturally provide references to such kind of intuitive and logical inference.
Thus, in this paper, we propose a new framework to leverage neural methods to
answer complex logical queries based on an EVKG, which can satisfy not only
traditional first-order logic constraints but also implicit logical constraints
over eventualities concerning their occurrences and orders. For instance, if we
know that "Food is bad" happens before "PersonX adds soy sauce", then "PersonX
adds soy sauce" is unlikely to be the cause of "Food is bad" due to implicit
temporal constraint. To facilitate consistent reasoning on EVKGs, we propose
Complex Eventuality Query Answering (CEQA), a more rigorous definition of CQA
that considers the implicit logical constraints governing the temporal order
and occurrence of eventualities. In this manner, we propose to leverage theorem
provers for constructing benchmark datasets to ensure the answers satisfy
implicit logical constraints. We also propose a Memory-Enhanced Query Encoding
(MEQE) approach to significantly improve the performance of state-of-the-art
neural query encoders on the CEQA task.
