Knowledge graphs contain informative factual knowledge but are considered incomplete. To answer complex queries under incomplete knowledge, learning-based Complex Query Answering (CQA) models are proposed to directly learn from the query-answer samples to avoid the direct traversal of incomplete graph data. Existing works formulate the training of complex query answering models as multi-task learning and require a large number of training samples. In this work, we explore the compositional structure of complex queries and argue that the different logical operator types, rather than the different complex query types, are the key to improving generalizability. Accordingly, we propose a meta-learning algorithm to learn the meta-operators with limited data and adapt them to different instances of operators under various complex queries. Empirical results show that learning meta-operators is more effective than learning original CQA or meta-CQA models.

翻译：知识图谱包含丰富的事实知识，但被认为是不完整的。为了在不完整知识下回答复杂查询，基于学习的复杂查询回答（CQA）模型被提出，通过直接学习查询-答案样本，避免直接遍历不完整的图数据。现有工作将复杂查询回答模型的训练表述为多任务学习，并需要大量训练样本。在本工作中，我们探索复杂查询的组成结构，并论证不同的逻辑操作符类型（而非不同的复杂查询类型）是提升泛化能力的关键。据此，我们提出一种元学习算法，用于在有限数据下学习元操作符，并将其适配到各种复杂查询下的不同操作符实例中。实验结果表明，学习元操作符比学习原始CQA模型或元CQA模型更为有效。