Research map / Large language models / Retrieval and knowledge
MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use MeTeoR instead of the query-rewriting baseline | Non-recursive LUBM temporal reasoning · LUBM temporal, D_L^4 | runtime: 683.0s -> 170.3s; 1575.6s -> 582.7s; 2254.6s -> 953.4s | Helped |
| Use MeTeoR instead of the query-rewriting baseline | Non-recursive meteorological reasoning · Meteorological data, D_W^62 | runtime: 2234.0s -> 1050.8s; 258.5s -> 240.4s | Helped |
| Use materialisation for queries answerable before automata reasoning | MeTeoR fact entailment · LUBM temporal | runtime | Helped |
| Use sorted interval lists and scan them for temporal joins | MeTeoR non-recursive reasoning · LUBM temporal, increasing temporal joins | runtime | Helped |
| Scale materialisation to larger recursive datasets | MeTeoR · LUBM temporal, up to 150 million facts | scalability: 150 million temporal facts | Helped |
Closest papers by meaning
- Goal-Driven Reasoning in DatalogMTL with Magic Sets (AAAI 2025 · similarity 0.88)
- Optimised Storage for Datalog Reasoning (AAAI 2024 · similarity 0.85)
- LogicCat: A Chain-of-Thought Text-to-SQL Benchmark for Complex Reasoning (AAAI 2026 · similarity 0.85)
- Temporal Conjunctive Query Answering via Rewriting (AAAI 2025 · similarity 0.84)
- Computing the Why-Provenance for Datalog Queries via SAT Solvers (AAAI 2024 · similarity 0.82)
- TimE: A Multi-level Benchmark for Temporal Reasoning of LLMs in Real-World Scenarios (NeurIPS 2025 · similarity 0.82)
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