Complex Query Answering (CQA) over Knowledge Graphs (KGs) has attracted a lot of attention to potentially support many applications. Given that KGs are usually incomplete, neural models are proposed to answer the logical queries by parameterizing set operators with complex neural networks. However, such methods usually train neural set operators with a large number of entity and relation embeddings from the zero, where whether and how the embeddings or the neural set operators contribute to the performance remains not clear. In this paper, we propose a simple framework for complex query answering that decomposes the KG embeddings from neural set operators. We propose to represent the complex queries into the query graph. On top of the query graph, we propose the Logical Message Passing Neural Network (LMPNN) that connects the local one-hop inferences on atomic formulas to the global logical reasoning for complex query answering. We leverage existing effective KG embeddings to conduct one-hop inferences on atomic formulas, the results of which are regarded as the messages passed in LMPNN. The reasoning process over the overall logical formulas is turned into the forward pass of LMPNN that incrementally aggregates local information to finally predict the answers' embeddings. The complex logical inference across different types of queries will then be learned from training examples based on the LMPNN architecture. Theoretically, our query-graph represenation is more general than the prevailing operator-tree formulation, so our approach applies to a broader range of complex KG queries. Empirically, our approach yields the new state-of-the-art neural CQA model. Our research bridges the gap between complex KG query answering tasks and the long-standing achievements of knowledge graph representation learning.

翻译：知识图谱上的复杂查询回答（CQA）因能潜在支持众多应用而备受关注。鉴于知识图谱通常不完备，研究者提出通过用复杂神经网络参数化集合算子的神经模型来回答逻辑查询。然而，这类方法通常从零开始用大量实体和关系嵌入训练神经集合算子，其中嵌入或神经集合算子是否以及如何对性能做出贡献仍不明确。本文提出一个简洁的复杂查询回答框架，将知识图谱嵌入与神经集合算子解耦。我们提出将复杂查询表示为查询图。在此查询图基础上，我们提出逻辑消息传递神经网络（LMPNN），该网络将原子公式上的局部单跳推理与整体逻辑推理相连接，以完成复杂查询回答。我们利用现有高效的知识图谱嵌入对原子公式进行单跳推理，其结果被视为LMPNN中传递的消息。整个逻辑公式的推理过程转化为LMPNN的前向传播，该过程逐步聚合局部信息，最终预测答案的嵌入。基于LMPNN架构，不同查询类型间的复杂逻辑推理将从训练样本中学习得到。理论上，我们的查询图表示比主流的算子树形式更具一般性，因此本方法适用于更广泛的复杂知识图谱查询。实验上，本方法产生了当前最先进的神经CQA模型。我们的研究弥合了复杂知识图谱查询回答任务与知识图谱表示学习长期成就之间的差距。