ODL-TempLLM: Ontology-Guided and Description Logic-Reasoned Temporal Reasoning with LLMs

Abstract

Temporal reasoning is crucial for large language models (LLMs) to understand event concurrency and complex temporal interactions in natural language. Recent approaches rely on the LLM to infer temporal relations between events and largely overlook the inherent structural nature of temporal relationships. In this work, we propose ODL-TempLLM ( O ntology-Guided and D escription L ogic–Constrained Temp oral Reasoning with LLM s), a novel paradigm for temporal reasoning with LLMs that shifts focus from internal inference to the explicit modeling of temporal structure. ODL-TempLLM leverages ontology learning to explicitly construct structured temporal knowledge, employs a symbolic reasoner to deductively reason about temporal relations and uses logic-constrained retrieval augmentation to obtain relevant facts. Experiments re-sults evaluated across three datasets via various LLM backbones show that our method outperforms state-of-the-art methods by 2.07–31.83 F1 points and 1.00–30.73 EM points, exhibiting strong generalization and highlighting the potential of explicit temporal reasoning.