Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction

Anonymous

TL;DR: We introduce Text2NKG, a novel fine-grained n-ary relation extraction framework 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 course-grained level, which is always in a single schema, ignoring the order of entities 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 $F_1$ scores on the fine-grained n-ary relation extraction benchmark. Our code and datasets are publicly available.

Paper Type: long

Research Area: NLP Applications

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Reproduction study, Approaches low compute settings-efficiency, Publicly available software and/or pre-trained models, Data resources, Data analysis, Theory

Languages Studied: English, Chinese