@inproceedings{wei-etal-2025-dtdes,
    title = "{DTDES}-{KGE}: Dual-Teacher Knowledge Distillation with Distinct Embedding Spaces for Knowledge Graph Embeddings",
    author = "Wei, Bofan  and
      Xu, Hongyuan  and
      Niu, Yuhang  and
      Ren, Jiarui  and
      Wen, Yanlong  and
      Yuan, Xiaojie",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.262/",
    doi = "10.18653/v1/2025.findings-emnlp.262",
    pages = "4875--4887",
    ISBN = "979-8-89176-335-7",
    abstract = "Knowledge distillation for knowledge graph embedding (KGE) models effectively compresses KGE models by reducing their embedding dimensions. While existing methods distill knowledge from a high-dimensional teacher to a low-dimensional student, they typically rely on a single teacher embedding space, thereby overlooking valuable complementary knowledge from teachers in distinct embedding spaces. This paper introduces DTDES-KGE, a novel knowledge distillation framework that significantly enhances distillation performance by leveraging dual teachers in distinct embedding spaces. To overcome the challenge of spatial heterogeneity when integrating knowledge from dual teachers, we propose a spatial compatibility module for reconciliation. Additionally, we introduce a student-aware knowledge fusion mechanism to fuse the knowledge from dual teachers dynamically. Extensive experiments on two real-world datasets validate the effectiveness of DTDES-KGE."
}