[1]张丽芳,李兴森.基元潜部特征元挖掘的智能方法研究[J].智能系统学报,2025,20(2):457-464.[doi:10.11992/tis.202310039]
ZHANG Lifang,LI Xingsen.Research on intelligent methods for latent features mining of basic element[J].CAAI Transactions on Intelligent Systems,2025,20(2):457-464.[doi:10.11992/tis.202310039]
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ZHANG Lifang,LI Xingsen.Research on intelligent methods for latent features mining of basic element[J].CAAI Transactions on Intelligent Systems,2025,20(2):457-464.[doi:10.11992/tis.202310039]
基元潜部特征元挖掘的智能方法研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
20
期数:
2025年第2期
页码:
457-464
栏目:
学术论文—人工智能基础
出版日期:
2025-03-05
- Title:
- Research on intelligent methods for latent features mining of basic element
- Keywords:
- extenics; latent feature element; feature element; basic-element theory; artificial intelligence; natural language processing; large language model; extension intelligence
- 分类号:
- TP18
- 摘要:
- 潜部特征元构建是可拓学基元理论的重要研究内容，潜在信息挖掘对解决问题和激发创新思维至关重要。为了解决目前人工识别基元潜部特征元效率低、覆盖面窄和数量不足的问题，研究基元潜部特征元显化理论与人工智能算法实现的结合点，提出挖掘基元潜部特征元的流程化、系统性方法，使用爬虫技术收集目标基元对象的相关信息，清洗噪音数据并从句子中挖掘构成特征元的名称和描述，用概率统计的定量方法筛选潜部特征元并通过Python代码实现智能挖掘功能，最后通过案例对比分析进行论证。研究结果能有效提高基元潜部特征元的识别效率和智能化水平，对从复杂多变的语料句法中进行语义概括也有一定的参考作用，为进一步提高特征名称及其量值智能提取的精确性积累训练集，促进可拓展型人工智能理论的发展。
- Abstract:
- Latent feature element construction is a key aspect of the basic-element theory of extenics, and mining latent information is crucial for problem solving and fostering innovative thinking. This study explores the integration of the basic-element latent feature element manifestation theory with artificial intelligence algorithms to address the current problems of low efficiency, narrow coverage and the insufficient number of manually identified basic-element latent feature elements. A process-oriented, systematic method for mining latent feature elements of basic elements is proposed. The method involves using crawler technology to collect relevant information regarding target basic-element objects, cleaning noisy data, and mining names and descriptions of constituent feature elements from sentences. A probability statistical approach is then used to filter latent feature elements, with the intelligent mining process implemented through Python code. Finally, a case study comparison is performed to demonstrate the effectiveness of this approach. Research results can notably improve the recognition efficiency and intelligence level of basic-element latent feature elements while also providing valuable insights for semantic generalization from complex and changeable dynamic corpus syntax. Additionally, it contributes to building a training set for enhancing the accuracy of intelligent extraction of feature names and their quantitative values, thus promoting the development of extensible artificial intelligence theory.
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备注/Memo
收稿日期:2023-10-27。
基金项目:国家自然科学基金项目(72071049); 广东省自然科学基金项目(2024A1515011324).
作者简介:张丽芳，软件研发工程师，主要研究方向为信息系统与电子商务。E-mail：f200553042@126.com;李兴森，教授，博士，中国人工智能学会理事，中国创造学会理事，中国人工智能学会可拓学专业委员会主任，主要研究方向为可拓学、知识管理与可拓智能创新。发表学术论文80余篇。E-mail：lixingsen@126.com。
通讯作者:李兴森. E-mail：lixingsen@126.com
更新日期/Last Update:
2025-03-05