Is Complex Query Answering Really Complex?
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866915369811181568 |
|---|---|
| author | Gregucci, Cosimo Xiong, Bo Hernandez, Daniel Loconte, Lorenzo Minervini, Pasquale Staab, Steffen Vergari, Antonio |
| author_facet | Gregucci, Cosimo Xiong, Bo Hernandez, Daniel Loconte, Lorenzo Minervini, Pasquale Staab, Steffen Vergari, Antonio |
| contents | Complex query answering (CQA) on knowledge graphs (KGs) is gaining momentum as a challenging reasoning task. In this paper, we show that the current benchmarks for CQA might not be as complex as we think, as the way they are built distorts our perception of progress in this field. For example, we find that in these benchmarks, most queries (up to 98% for some query types) can be reduced to simpler problems, e.g., link prediction, where only one link needs to be predicted. The performance of state-of-the-art CQA models decreases significantly when such models are evaluated on queries that cannot be reduced to easier types. Thus, we propose a set of more challenging benchmarks composed of queries that require models to reason over multiple hops and better reflect the construction of real-world KGs. In a systematic empirical investigation, the new benchmarks show that current methods leave much to be desired from current CQA methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12537 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Is Complex Query Answering Really Complex? Gregucci, Cosimo Xiong, Bo Hernandez, Daniel Loconte, Lorenzo Minervini, Pasquale Staab, Steffen Vergari, Antonio Machine Learning Artificial Intelligence Complex query answering (CQA) on knowledge graphs (KGs) is gaining momentum as a challenging reasoning task. In this paper, we show that the current benchmarks for CQA might not be as complex as we think, as the way they are built distorts our perception of progress in this field. For example, we find that in these benchmarks, most queries (up to 98% for some query types) can be reduced to simpler problems, e.g., link prediction, where only one link needs to be predicted. The performance of state-of-the-art CQA models decreases significantly when such models are evaluated on queries that cannot be reduced to easier types. Thus, we propose a set of more challenging benchmarks composed of queries that require models to reason over multiple hops and better reflect the construction of real-world KGs. In a systematic empirical investigation, the new benchmarks show that current methods leave much to be desired from current CQA methods. |
| title | Is Complex Query Answering Really Complex? |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2410.12537 |