@inproceedings{liu-etal-2026-odl,
    title = "{ODL}-{T}emp{LLM}: Ontology-Guided and Description Logic-Reasoned Temporal Reasoning with {LLM}s",
    author = "Liu, Jinshuo  and
      Bi, Cheng  and
      Wang, Meng  and
      Deng, Juan  and
      Ji, Donghong  and
      Pan, Jeff Z.",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.873/",
    pages = "19113--19126",
    ISBN = "979-8-89176-390-6",
    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 (Ontology-Guided and Description Logic{--}Constrained Temporal Reasoning with LLMs), 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 results evaluated across there 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."
}