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

TL;DR

Text2NKG is a novel framework for fine-grained n-ary relation extraction that constructs more detailed and flexible n-ary knowledge graphs, outperforming previous methods in accuracy.

Contribution

It introduces a span-tuple classification approach with hetero-ordered merging for extracting n-ary relations across multiple schemas.

Findings

Achieves state-of-the-art F1 scores on n-ary relation extraction benchmarks.

Supports four types of NKG schemas with high flexibility.

Demonstrates effective fine-grained relation extraction in real-world scenarios.

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.