MusREL

IF 4.1 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal on Semantic Web and Information Systems Pub Date : 2023-09-08 DOI:10.4018/ijswis.329965

Zhen Zhu, Huaiyuan Lin, Dongmei Gu, Liting Wang, Hong Wu, Yun Fang

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引用次数: 0

Abstract

In order to enhance the utility of online educational digital resources, the authors propose a practical and efficient multi-strategy relation extraction (RE) model in online education scenarios. First, the effective relation discrimination model is used to make relation predictions for non-structured teaching resources and eliminate the noise data. Then, they extract relations from different path strategies using multiple low-computational resources and efficient relation extraction strategies and use their proposed multi-strategy weighting calculator to weigh the relation extraction strategies to derive the final target relations. To cope with the low-resource relation extraction scenario, the relation extraction results are complemented by using prompt learning with a big model paradigm. They also consider the model to serve the commercial scenario of online education, and they propose a global rate controller to adjust and adapt the rate and throughput requirements in different scenarios, so as to achieve the best balance of system stability, computation speed, and extraction performance.

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为了提高在线教育数字资源的效用，提出了一种实用高效的在线教育场景多策略关系抽取模型。首先，利用有效关系判别模型对非结构化教学资源进行关系预测，剔除噪声数据;然后，他们利用多种低计算资源和高效的关系提取策略从不同的路径策略中提取关系，并使用他们提出的多策略加权计算器对这些关系提取策略进行加权，从而得到最终的目标关系。为应对低资源关系提取场景，利用大模型范式的提示学习对关系提取结果进行补充。他们还考虑了该模型服务于在线教育的商业场景，并提出了一个全局速率控制器来调整和适应不同场景下的速率和吞吐量需求，从而达到系统稳定性、计算速度和提取性能的最佳平衡。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal on Semantic Web and Information Systems 工程技术-计算机：人工智能

CiteScore

6.20

自引率

12.50%

发文量

审稿时长

20 months

期刊介绍： The International Journal on Semantic Web and Information Systems (IJSWIS) promotes a knowledge transfer channel where academics, practitioners, and researchers can discuss, analyze, criticize, synthesize, communicate, elaborate, and simplify the more-than-promising technology of the semantic Web in the context of information systems. The journal aims to establish value-adding knowledge transfer and personal development channels in three distinctive areas: academia, industry, and government.