@inproceedings{zhu-2025-thesis,
    title = "Thesis Proposal: Uncertainty in Knowledge Graph Embeddings",
    author = "Zhu, Yuqicheng",
    editor = "Ebrahimi, Abteen  and
      Haider, Samar  and
      Liu, Emmy  and
      Haider, Sammar  and
      Leonor Pacheco, Maria  and
      Wein, Shira",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)",
    month = apr,
    year = "2025",
    address = "Albuquerque, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2025.naacl-srw.4/",
    doi = "10.18653/v1/2025.naacl-srw.4",
    pages = "40--47",
    ISBN = "979-8-89176-192-6",
    abstract = "Knowledge Graph Embedding (KGE) methods are widely used to map entities and relations from knowledge graphs (KGs) into continuous vector spaces, enabling non-classical reasoning over knowledge structures. Despite their effectiveness, the uncertainty of KGE methods has not been extensively studied in the literature. This gap poses significant challenges, particularly when deploying KGE models in high-stakes domains like medicine, where reliability and risk assessment are critical. This dissertation seeks to investigate various types of uncertainty in KGE methods and explore strategies to quantify, mitigate, and reason under uncertainty effectively. The outcomes of this research will contribute to enhancing the reliability of KGE methods, providing greater confidence in their use beyond benchmark datasets, and supporting their application in real-world, high-stakes domains."
}