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。

超关联知识图谱的基数估计

Cardinality Estimation on Hyper-relational Knowledge Graphs

Stemming from traditional knowledge graphs (KGs), hyper-relational KGs (HKGs)
provide additional key-value pairs (i.e., qualifiers) for each KG fact that
help to better restrict the fact validity. In recent years, there has been an
increasing interest in studying graph reasoning over HKGs. In the meantime, due
to the ever-evolving nature of world knowledge, extensive parallel works have
been focusing on reasoning over temporal KGs (TKGs), where each TKG fact can be
viewed as a KG fact coupled with a timestamp (or time period) specifying its
time validity. The existing HKG reasoning approaches do not consider temporal
information because it is not explicitly specified in previous benchmark
datasets. Besides, all the previous TKG reasoning methods only lay emphasis on
temporal reasoning and have no way to learn from qualifiers. To this end, we
aim to fill the gap between TKG reasoning and HKG reasoning. We develop two new
benchmark hyper-relational TKG (HTKG) datasets, i.e., Wiki-hy and YAGO-hy, and
propose a HTKG reasoning model that efficiently models both temporal facts and
qualifiers. We further exploit additional time-invariant relational knowledge
from the Wikidata knowledge base and study its effectiveness in HTKG reasoning.
Time-invariant relational knowledge serves as the knowledge that remains
unchanged in time (e.g., Sasha Obama is the child of Barack Obama), and it has
never been fully explored in previous TKG reasoning benchmarks and approaches.
Experimental results show that our model substantially outperforms previous
related methods on HTKG link prediction and can be enhanced by jointly
leveraging both temporal and time-invariant relational knowledge.

我们填补了时态图谱推理和超关系时态图谱推理之间的差距，通过开发两个新的基准数据集以及提出一个高效模型，同时建模时间事实和限定词，并探索从 Wikidata knowledge base 中获取的额外的不随时间变化的关系知识对于高效推理的有效性。