Cardinality Estimation (CE) for query is to estimate the number of results
without execution, which is an effective index in query optimization. Recently,
CE over has achieved great success in knowledge graphs (KGs) that consist of
triple facts. To more precisely represent facts, current researchers propose
hyper-relational KGs (HKGs) to represent a triple fact with qualifiers, where
qualifiers provide additional context to the fact. However, existing CE methods
over KGs achieve unsatisfying performance on HKGs due to the complexity of
qualifiers in HKGs. Also, there is only one dataset for HKG query cardinality
estimation, i.e., WD50K-QE, which is not comprehensive and only covers limited
patterns. The lack of querysets over HKG also becomes a bottleneck to
comprehensively investigate CE problems on HKGs. In this work, we first
construct diverse and unbiased hyper-relational querysets over three popular
HKGs for investigating CE. Besides, we also propose a novel qualifier-attached
graph neural network (GNN) model that effectively incorporates qualifier
information and adaptively combines outputs from multiple GNN layers, to
accurately predict the cardinality. Our experiments illustrate that the
proposed hyper-relational query encoder outperforms all state-of-the-art CE
methods over three popular HKGs on the diverse and unbiased benchmark.

本研究旨在通过构建多样化和无偏见的超关系查询集，综合调查超关系知识图上的查询基数估计问题，并提出一种新颖的附加限定词图神经网络（GNN）模型，该模型能够有效地将限定词信息整合，并自适应地合并多个 GNN 层的输出，以准确预测查询基数。实验证明，所提出的超关系查询编码器在多样化和无偏见的基准测试上优于所有现有的超关系知识图上的查询基数估计方法。