@inproceedings{akgul-etal-2026-recipe,
    title = "{RECIPE}-{TKG}: From Sparse History to Structured Reasoning for {LLM}-based Temporal Knowledge Graph Completion",
    author = {Akg{\"u}l, {\"O}mer Faruk  and
      Zhu, Feiyu  and
      Yang, Yuxin  and
      Kannan, Rajgopal  and
      Prasanna, Viktor},
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.86/",
    pages = "1943--1965",
    ISBN = "979-8-89176-380-7",
    abstract = "Temporal Knowledge Graphs (TKGs) represent dynamic facts as timestamped relations between entities. While Large Language Models (LLMs) show promise for TKG completion, current approaches typically apply generic pipelines (neighborhood sampling, supervised fine-tuning, uncalibrated inference) without task-specific adaptation to temporal relational reasoning. Through systematic analysis under unified evaluation, we reveal three key failure modes: (1) retrieval strategies miss multi-hop dependencies when target entities are not directly observed in history, (2) standard fine-tuning reinforces memorization over relational generalization, and (3) uncalibrated generation produces contextually implausible entities. We present RECIPE-TKG, a parameter-efficient framework that addresses each limitation through principled, task-specific design: rule-based multi-hop sampling for structural grounding, contrastive fine-tuning to shape relational compatibility, and test-time semantic filtering for contextual alignment. Experiments on four benchmarks show that RECIPE-TKG outperforms prior LLM-based methods across input regimes, achieving up to 22.4{\%} relative improvement in Hits@10, with particularly strong gains when historical evidence is sparse or indirect."
}