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.

翻译：采用深度学习技术对不完整知识图谱（KGs）进行查询，可自然利用推理与泛化能力来学习推断更优答案。传统神经复杂查询回答（CQA）方法主要关注实体为中心的KGs。然而在现实世界中，我们还需对事件、状态和活动（即事件性/情境）进行逻辑推理，以推动学习系统从系统I向系统II演进（Yoshua Bengio提出）。基于事件性知识图谱（EVKG）的逻辑查询可为此类直觉与逻辑推理提供天然参照。因此，本文提出一种新框架，利用神经方法回答基于EVKG的复杂逻辑查询，该框架既能满足传统一阶逻辑约束，又能处理事件性中关于事件发生与顺序的隐式逻辑约束。例如，若已知"食物变质"发生在"某人加酱油"之前，则根据隐式时间约束，"某人加酱油"不太可能是食物变质的原因。为实现EVKG上的一致性推理，我们提出复杂事件性查询回答（CEQA）——一种更严格的CQA定义，其将控制事件时间顺序与发生条件的隐式逻辑约束纳入考量。基于此，我们利用定理证明器构建基准数据集，确保答案满足隐式逻辑约束。我们还提出记忆增强查询编码（MEQE）方法，显著提升现有神经查询编码器在CEQA任务上的性能。