Research on Entity Relation Extraction Method Based on Relational Prompts with Single-Module Single-Step Approach

LIU Hui; ZHANG Zhi; WANG Qiyuan

doi:10.7652/xjtuxb202503020

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Research on Entity Relation Extraction Method Based on Relational Prompts with Single-Module Single-Step Approach

更新时间：2026-01-06

- Research on Entity Relation Extraction Method Based on Relational Prompts with Single-Module Single-Step Approach
- Journal of Xi’an Jiaotong University Vol. 59, Issue 3, Pages: 222-234(2025)
- 作者机构：
  
  1.国防科技大学电子对抗学院， 230037，合肥
  2.哈尔滨工业大学计算机科学与技术学院， 150001，哈尔滨
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202503020
  CLC： TP391.1
- Received：01 July 2024，
  
  Online First：24 October 2024，
  
  Published：10 March 2025
- 稿件说明：
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LIU Hui, ZHANG Zhi, WANG Qiyuan. Research on Entity Relation Extraction Method Based on Relational Prompts with Single-Module Single-Step Approach[J]. Journal of Xi’an Jiaotong University, 2025, 59(3): 222-234.
DOI：

LIU Hui, ZHANG Zhi, WANG Qiyuan. Research on Entity Relation Extraction Method Based on Relational Prompts with Single-Module Single-Step Approach[J]. Journal of Xi’an Jiaotong University, 2025, 59(3): 222-234. DOI： 10.7652/xjtuxb202503020.

摘要

针对现有关系三元组抽取方法由于忽略关系本身的关系语义信息以及三元组中元素的相互依赖和不可分性所导致的抽取效果不佳问题，提出了一种基于关系提示的实体关系抽取方法。在构建单模块单步关系三重抽取模型（RPSS）的基础上，考虑不同层次的关系语义信息和符号级和特征级的关系提示信息，对实体和关系提示符进行联合编码，得到统一的全局表示；同时通过注意力机制挖掘不同嵌入之间的深层关联，构建三重交互矩阵，可在一个步骤中直接从单个模块中提取所有三元组。结果表明：所提方法在NYT、WebNLG两个基准数据集上实现了最佳的表现，

分别达到了93.3%和94.9%。

Abstract

To address the issue of suboptimal performance in existing relation triplet extraction methods

caused by the oversight of relationship semantic information and the interdependence and indivisibility of elements in triplets

a novel entity relation extraction method based on relational prompts is proposed. Building upon a single-module single-step relation triple extraction model (RPSS)

this method incorporates relationship semantic information at varying levels and symbol-level as well as feature-level relational prompt information. By jointly encoding entities and relational prompt symbols to derive a unified global representation

and employing attention mechanisms to delve into deep associations among different embeddings

a triple interaction matrix is constructed. This approach enables the extraction of all triplets directly from a single module in a single step. The proposed method demonstrates superior performance on benchmark datasets like NYT and WebNLG

achieving

scores of 93.3% and 94.9%

respectively.

关键词

Keywords

references

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