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一种用于语义知识的混合提取模型 发现水资源保护 大数据
Yanna Feng1, Feng Zhang2, Yongheng Zhang2
1School of Management, Yulin University, Yulin, Shannxi, China.
PeerJ. Computer science
|September 24, 2025
概括
一个新的深度学习模型 (WIEM-DL) 通过提高网络数据提取效率和准确性来增强水资源保护的公众论分析. 这种先进的方法可以提炼内容,过噪音,并提高整体性能.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 现有的网络数据提取方法用于多源数据集的节水面临效率低下和可扩展性问题.
- 分析公众对节水的看法需要高效和准确的数据处理技术.
- 在环境领域对复杂的论分析的需求正在迅速增加.
研究的目的:
- 开发一种基于深度学习 (WIEM-DL) 的新型,可转移的网络信息提取模型,用于分析水资源保护方面的公众意见.
- 提高自动化从多源数据集中提取网络数据的效率,准确性和可扩展性.
- 建立微层次论信息提取和特征表示的方法,用于数据层面的整合.
主要方法:
- 集成大数据分析,自然语言处理和深度学习.
- 开发基于深度学习的网络信息提取模型 (WIEM-DL),利用知识图,机器学习和基于本体学的方法.
- 精制与节水相关的在线公众论内容,并从关键句子中提取关键特征,以过噪音并定位主要内容.
主要成果:
- WIEM-DL模型展示了适应不同网站结构的适应性,以有效地跨网站提取信息.
- 实现了处理时间的显著减少以及提取精度和效率的显著提高.
- 该模型成功建立了微层次论提取和特征表示的方法,为数据集成创造了融合空间.
结论:
- WIEM-DL模型为公众论大数据中的多细分语义知识集成提供了坚实的基础.
- 实验结果证实,WIEM-DL的性能大大优于传统的信息提取方法.
- 这项研究为保护水资源的公众论分析领域的信息提取性能设定了新的基准.
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