Cardinality Estimation on Hyper-relational Knowledge Graphs

TL;DR

This paper introduces a new benchmark and a qualifier-aware GNN model for more accurate cardinality estimation on hyper-relational knowledge graphs, addressing the limitations of existing methods.

Contribution

The work constructs diverse querysets for hyper-relational KGs and proposes a novel GNN model that effectively utilizes qualifier information for improved estimation accuracy.

Findings

The proposed model outperforms existing methods on three benchmarks.

Constructed diverse and unbiased querysets for hyper-relational KGs.

Demonstrated the effectiveness of qualifier-aware GNN in cardinality estimation.

Abstract

Cardinality Estimation (CE) for query is to estimate the number of results without execution, which is an effective index in query optimization. Recently, CE for queries over knowlege graph (KGs) with triple facts has achieved great success. To more precisely represent facts, current researchers propose hyper-relational KGs (HKGs) to represent a triple fact with qualifiers providing additional context to the fact. However, existing CE methods, such as sampling and summary methods over KGs, perform unsatisfactorily on HKGs due to the complexity of qualifiers. Learning-based CE methods do not utilize qualifier information to learn query representation accurately, leading to poor performance. Also, there is only one limited CE benchmark for HKG query, 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-aware 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 demonstrate that our model outperforms all state-of-the-art CE methods over three benchmarks on popular HKGs.