@inproceedings{yang-etal-2025-daea,
    title = "{DAEA}: Enhancing Entity Alignment in Real-World Knowledge Graphs Through Multi-Source Domain Adaptation",
    author = "Yang, Linyan  and
      Zhou, Shiqiao  and
      Cheng, Jingwei  and
      Zhang, Fu  and
      Wan, Jizheng  and
      Wang, Shuo  and
      Lee, Mark",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.coling-main.393/",
    pages = "5890--5901",
    abstract = "Entity Alignment (EA) is a critical task in Knowledge Graph (KG) integration, aimed at identifying and matching equivalent entities that represent the same real-world objects. While EA methods based on knowledge representation learning have shown strong performance on synthetic benchmark datasets such as DBP15K, their effectiveness significantly decline in real-world scenarios which often involve data that is highly heterogeneous, incomplete, and domain-specific, as seen in datasets like DOREMUS and AGROLD. Addressing this challenge, we propose DAEA, a novel EA approach with Domain Adaptation that leverages the data characteristics of synthetic benchmarks for improved performance in real-world datasets. DAEA introduces a multi-source KGs selection mechanism and a specialized domain adaptive entity alignment loss function to bridge the gap between real-world data and optimal benchmark data, mitigating the challenges posed by aligning entities across highly heterogeneous KGs. Experimental results demonstrate that DAEA outperforms state-of-the-art models on real-world datasets, achieving a 29.94{\%} improvement in Hits@1 on DOREMUS and a 5.64{\%} improvement on AGROLD. Code is available at https://github.com/yangxiaoxiaoly/DAEA."
}