@inproceedings{xie-etal-2026-maki,
    title = "{MAKI}: Multi-layer Aligned Knowledge Injection for Structure-aware Knowledge Graph Completion with Large Language Models",
    author = "Xie, Zhiwen  and
      Wang, Xin  and
      Zhou, Guangyou  and
      Wong, Derek F.",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1423/",
    pages = "28527--28539",
    ISBN = "979-8-89176-395-1",
    abstract = "Recent advances in large language models (LLMs) have shown strong potential for knowledge graph completion (KGC). However, existing LLM-based approaches often struggle to effectively capture the structural information in knowledge graphs (KGs), leading to suboptimal reasoning performance. To address this challenge, we propose a Multi-layer Aligned Knowledge Injection (MAKI) model, a novel method that tightly integrates structured KG information into LLMs through multi-layer alignment. Specifically, we first leverage LLMs to encode the textual information of entities and relations, obtaining their semantic representations across multiple hidden layers. We then introduce a multi-layer aligned structure learning module, which uses graph neural networks (GNNs) to learn relational structures while aligning with the corresponding LLM layers to bridge the gap between structural and semantic spaces. Finally, a gated fusion mechanism is used to inject the structured knowledge into the LLM for reasoning over candidate triples. Experimental results on various benchmark datasets demonstrate that the proposed MAKI outperforms existing state-of-the-art methods."
}