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.

翻译：利用深度学习方法查询知识图谱（KGs）可自然发挥推理与泛化能力，从而学习推断更准确的答案。传统神经复杂查询回答（CQA）方法大多基于实体中心型知识图谱。然而，现实世界中，我们还需对事件、状态与活动（即事件或情境）进行逻辑推理，以推动学习系统从系统I向系统II演进（如Yoshua Bengio所提出）。从事件中心型知识图谱（EVKG）进行逻辑查询，能为这类直觉与逻辑推理提供自然参考。因此，本文提出一个新框架，利用神经方法在EVKG上回答复杂逻辑查询，该框架不仅能够满足传统一阶逻辑约束，还能处理关于事件发生及其顺序的隐式逻辑约束。例如，若已知"食物变质"发生在"PersonX添加酱油"之前，则因隐式时间约束，"PersonX添加酱油"不太可能是"食物变质"的原因。为了促进EVKG上的一致性推理，我们提出复杂事件查询回答（CEQA）——一种更严格的CQA定义，它考虑约束事件时间顺序与发生的隐式逻辑约束。基于此，我们建议利用定理证明器构建基准数据集，以确保答案满足隐式逻辑约束。此外，我们还提出一种记忆增强查询编码（MEQE）方法，显著提升现有最先进神经查询编码器在CEQA任务上的性能。