Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction
Abstract
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.
Cite
@article{arxiv.2310.05185,
title = {Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction},
author = {Haoran Luo and Haihong E and Yuhao Yang and Tianyu Yao and Yikai Guo and Zichen Tang and Wentai Zhang and Kaiyang Wan and Shiyao Peng and Meina Song and Wei Lin and Yifan Zhu and Luu Anh Tuan},
journal= {arXiv preprint arXiv:2310.05185},
year = {2024}
}
Comments
Accepted by NeurIPS 2024 main conference