Summary of Is Complex Query Answering Really Complex?, by Cosimo Gregucci et al.
Is Complex Query Answering Really Complex?
by Cosimo Gregucci, Bo Xiong, Daniel Hernandez, Lorenzo Loconte, Pasquale Minervini, Steffen Staab, Antonio Vergari
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Medium Difficulty summary: Complex query answering (CQA) on knowledge graphs (KGs) is an emerging challenge in artificial intelligence, with a focus on reasoning and querying large-scale graph-based data structures. The paper reveals that the commonly used benchmarks for CQA may not accurately reflect the complexity of this task, as many queries can be reduced to simpler problems like link prediction. State-of-the-art models struggle when evaluated on more complex queries that require multi-hop reasoning. To better evaluate CQA methods, the authors propose a new set of challenging benchmarks that simulate real-world KG construction and demonstrate the limitations of current approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Researchers are trying to figure out how well computers can answer complex questions about big datasets called knowledge graphs. They found that most “hard” questions can be broken down into simpler ones, making it seem like computers are better at this task than they really are. When they test these computers on harder questions, they actually don’t do very well. To make things fair and challenging for computer models, the researchers created new benchmarks that mimic real-world situations and show how far we still have to go. |
