A complex query answering method reduces symbolic search to top-k candidate domains and uses approximate local search for cycles, reaching near-FIT accuracy at a fraction of the cost.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Efficient and Scalable Neural Symbolic Search for Knowledge Graph Complex Query Answering
A complex query answering method reduces symbolic search to top-k candidate domains and uses approximate local search for cycles, reaching near-FIT accuracy at a fraction of the cost.