Summary of Cardinality Estimation on Hyper-relational Knowledge Graphs, by Fei Teng et al.
Cardinality Estimation on Hyper-relational Knowledge Graphs
by Fei Teng, Haoyang Li, Shimin Di, Lei Chen
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach develops a novel Cardinality Estimation (CE) method for queries over hyper-relational knowledge graphs (HKGs), which represent triple facts with qualifiers. The existing CE methods, including sampling and summary methods, perform poorly due to the complexity of qualifiers. To address this limitation, the authors propose a qualifier-aware graph neural network (GNN) model that incorporates qualifier information and adaptively combines outputs from multiple GNN layers. The model is evaluated on three benchmarks over popular HKGs, outperforming state-of-the-art CE methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cardinality Estimation (CE) helps optimize queries by guessing the number of results without executing them. Researchers have made progress on CEs for knowledge graphs with triple facts, but these methods don’t work well when considering qualifiers that add context to each fact. To solve this problem, scientists propose a new method that uses a special kind of neural network called a graph neural network (GNN). The GNN model takes into account the qualifier information and combines its results in a smart way. This approach is tested on three popular knowledge graphs and shows better performance than current methods. |
Keywords
» Artificial intelligence » Gnn » Graph neural network » Neural network
