With the continuous growth of large Knowledge Graphs (KGs), extractive KG summarization becomes a trending task. Aiming at distilling a compact subgraph with condensed information, it facilitates various downstream KG-based tasks. In this survey paper, we are among the first to provide a systematic overview of its applications and define a taxonomy for existing methods from its interdisciplinary studies. Future directions are also laid out based on our extensive and comparative review.

翻译：随着大型知识图谱(Knowledge Graphs, KGs)的持续增长，提取式知识图谱摘要已成为一个前沿研究任务。该任务旨在提取包含浓缩信息的紧凑子图，从而促进各类基于知识图谱的下游任务。在本综述中，我们率先系统梳理了其应用场景，并基于跨学科研究为现有方法定义了分类体系。在全面比较分析的基础上，我们还展望了未来研究方向。