AI RESEARCH
Efficient and Scalable Neural Symbolic Search for Knowledge Graph Complex Query Answering
arXiv CS.AI
•
ArXi:2505.08155v4 Announce Type: replace 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 complexity for cyclic queries. Consequently, these approaches struggle to scale effectively to large knowledge graphs and complex queries.