Beyond traditional binary relational facts, n-ary relational knowledge graphs (NKGs) are comprised of n-ary relational facts containing more than two entities, which are closer to real-world facts with broader applications. However, the construction of NKGs still significantly relies on manual labor, and n-ary relation extraction still remains at a course-grained level, which is always in a single schema and fixed arity of entities. To address these restrictions, we propose Text2NKG, a novel fine-grained n-ary relation extraction framework for n-ary relational knowledge graph construction. We introduce a span-tuple classification approach with hetero-ordered merging to accomplish fine-grained n-ary relation extraction in different arity. Furthermore, Text2NKG supports four typical NKG schemas: hyper-relational schema, event-based schema, role-based schema, and hypergraph-based schema, with high flexibility and practicality. Experimental results demonstrate that Text2NKG outperforms the previous state-of-the-art model by nearly 20\% points in the $F_1$ scores on the fine-grained n-ary relation extraction benchmark in the hyper-relational schema. Our code and datasets are publicly available.

翻译：超越传统二元关系事实，N元关系知识图谱由包含两个以上实体的N元关系事实构成，这些事实更贴近现实世界，具有更广泛的应用场景。然而，N元关系知识图谱的构建仍高度依赖人工，且现有N元关系抽取技术始终停留在粗粒度层面，通常采用单一模式且实体元数固定。为解决上述限制，我们提出Text2NKG——一种面向N元关系知识图谱构建的新型细粒度N元关系抽取框架。通过引入异序合并的跨度-元组分类方法，实现了不同元数下细粒度N元关系的抽取。此外，Text2NKG支持超关系模式、事件模式、角色模式和超图模式四种典型N元知识图谱模式，具备高度灵活性与实用性。实验结果表明，在超关系模式的细粒度N元关系抽取基准测试中，Text2NKG的$F_1$分数相较先前最先进模型提升近20个百分点。我们的代码与数据集已公开。