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MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators

AAAI 2022 · Large language models › Retrieval and knowledge · approach: Knowledge graph reasoning with LLMs · 21 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use MeTeoR instead of the query-rewriting baselineNon-recursive LUBM temporal reasoning · LUBM temporal, D_L^4runtime: 683.0s -> 170.3s; 1575.6s -> 582.7s; 2254.6s -> 953.4sHelped
Use MeTeoR instead of the query-rewriting baselineNon-recursive meteorological reasoning · Meteorological data, D_W^62runtime: 2234.0s -> 1050.8s; 258.5s -> 240.4sHelped
Use materialisation for queries answerable before automata reasoningMeTeoR fact entailment · LUBM temporalruntimeHelped
Use sorted interval lists and scan them for temporal joinsMeTeoR non-recursive reasoning · LUBM temporal, increasing temporal joinsruntimeHelped
Scale materialisation to larger recursive datasetsMeTeoR · LUBM temporal, up to 150 million factsscalability: 150 million temporal factsHelped

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