@inproceedings{wang-etal-2025-sememic,
    title = "How Sememic Components Can Benefit Link Prediction for Lexico-Semantic Knowledge Graphs?",
    author = "Wang, Hansi  and
      Wang, Yue  and
      Liang, Qiliang  and
      Liu, Yang",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.740/",
    pages = "14665--14684",
    ISBN = "979-8-89176-332-6",
    abstract = "Link Prediction (LP) aims to predict missing triple information within a Knowledge Graph (KG). Existing LP methods have sought to improve the performance by integrating structural and textual information. However, for lexico-semantic KGs designed to document fine-grained sense distinctions, these types of information may not be sufficient to support effective LP. From a linguistic perspective, word senses within lexico-semantic relations usually show systematic differences in their sememic components. In light of this, we are motivated to enhance LP with sememe knowledge. We first construct a Sememe Prediction (SP) dataset, SememeDef, for learning such knowledge, and two Chinese datasets, HN7 and CWN5, for LP evaluation; Then, we propose a method, SememeLP, to leverage this knowledge for LP fully. It consistently and significantly improves the LP performance in both English and Chinese, achieving SOTA MRR of 75.1{\%}, 80.5{\%}, and 77.1{\%} on WN18RR, HN7, and CWN5, respectively; Finally, an in-depth analysis is conducted, making clear how sememic components can benefit LP for lexico-semantic KGs, which provides promising progress for the completion of them."
}