Querying incomplete 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.

本文研究基于事件中心知识图谱的深度神经网络复杂查询方法，提出了复杂事件查询回答（CEQA）框架，利用定理证明器填补实体中心查询系统无法推理的逻辑不确定性，实现了在具有时间维度的事件中心知识图谱上的准确推理。