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 remains at a coarse-grained level, which is always in a single schema, ignoring the order and variable 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 and output 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. The experimental results demonstrate that Text2NKG achieves state-of-the-art performance in F1 scores on the fine-grained n-ary relation extraction benchmark. Our code and datasets are publicly available.

翻译：超越传统的二元关系事实，N元关系知识图谱（NKGs）由包含多于两个实体的N元关系事实构成，这类事实更贴近现实世界，具有更广泛的应用前景。然而，当前的NKG构建仍停留在粗粒度层面，通常局限于单一模式，忽略了实体的顺序和可变元数。为应对这些限制，我们提出了Text2NKG，一个用于构建N元关系知识图谱的新型细粒度N元关系抽取框架。我们引入了一种结合异构有序合并与输出合并的跨度-元组分类方法，以实现不同元数下的细粒度N元关系抽取。此外，Text2NKG支持四种典型的NKG模式：超关系模式、基于事件的模式、基于角色的模式以及基于超图的模式，具有高度的灵活性和实用性。实验结果表明，Text2NKG在细粒度N元关系抽取基准测试的F1分数上达到了最先进的性能。我们的代码和数据集已公开。