Efficient and Scalable Neural Symbolic Search for Knowledge Graph Complex Query Answering 事件

Efficient and Scalable Neural Symbolic Search for Knowledge Graph Complex Query Answering arXiv:2505.08155v4 Announce Type: replace Abstract: Complex Query Answering (CQA) is a crucial reasoning task over Knowledge Graphs (KGs), which aims to answer first-order logical queries from incomplete KGs. While existing neural-symbolic methods achieve strong performance, they face significant complexity bottlenecks: quadratic data complexity scaling with the number of entities, and NP-hard query comple