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.
NLISA maintains a smaller fuzzy vector by preserving only the indices within the relevant domains, resulting in greater flexibility
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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.