Purpose:This paper compares the paradigmatic differences between knowledge organization(KO)in library and information science and knowledge representation(KR)in AI to show the convergence in KO and KR methods and appl...Purpose:This paper compares the paradigmatic differences between knowledge organization(KO)in library and information science and knowledge representation(KR)in AI to show the convergence in KO and KR methods and applications.Methodology:The literature review and comparative analysis of KO and KR paradigms is the primary method used in this paper.Findings:A key difference between KO and KR lays in the purpose of KO is to organize knowledge into certain structure for standardizing and/or normalizing the vocabulary of concepts and relations,while KR is problem-solving oriented.Differences between KO and KR are discussed based on the goal,methods,and functions.Research limitations:This is only a preliminary research with a case study as proof of concept.Practical implications:The paper articulates on the opportunities in applying KR and other AI methods and techniques to enhance the functions of KO.Originality/value:Ontologies and linked data as the evidence of the convergence of KO and KR paradigms provide theoretical and methodological support to innovate KO in the AI era.展开更多
人工智能科学(AI for science,AI4S)为代表的新范式正在重塑科学研究。作为AI4S的关键技术,大语言模型在教育智能体建模与仿真、教育过程挖掘、教育数据增广等方面展现出了巨大潜力。研究立足科学哲学领域的“问题-方法-过程”框架,剖...人工智能科学(AI for science,AI4S)为代表的新范式正在重塑科学研究。作为AI4S的关键技术,大语言模型在教育智能体建模与仿真、教育过程挖掘、教育数据增广等方面展现出了巨大潜力。研究立足科学哲学领域的“问题-方法-过程”框架,剖析了大语言模型引发的教育研究范式变革图景:在问题维度,大语言模型基于海量数据形成的“世界知识”拓宽了教育研究的问题视野;在方法维度,大语言模型依托其“泛思维链”能力,为情境建模、模拟仿真、因果推断等方法创新提供新的可能;在过程维度;大语言模型为“端到端”和“人在回路”理念在教育研究中的融合提供了理想的技术载体,开启了人机协同的新范式;结合教育研究范式演进的历史维度,当前AI4S引领的变革是社会需求牵引和技术进步双重驱动的必然,既延续了数字时代教育研究范式的演进逻辑,还在智能维度、生成式范式、跨界整合等方面实现了独特突破。需要指出的是,这场范式变革虽然前景广阔,但其复杂性也不容忽视。研究对教育知识生产“单一文化”、理解错觉加剧、模型黑箱效应等潜在风险作了深度探讨,提出了重塑教育研究的反思性、审慎评估大语言模型适用边界的策略实施,为应对AI4S时代的教育机遇与挑战提供了新思路。展开更多
文摘Purpose:This paper compares the paradigmatic differences between knowledge organization(KO)in library and information science and knowledge representation(KR)in AI to show the convergence in KO and KR methods and applications.Methodology:The literature review and comparative analysis of KO and KR paradigms is the primary method used in this paper.Findings:A key difference between KO and KR lays in the purpose of KO is to organize knowledge into certain structure for standardizing and/or normalizing the vocabulary of concepts and relations,while KR is problem-solving oriented.Differences between KO and KR are discussed based on the goal,methods,and functions.Research limitations:This is only a preliminary research with a case study as proof of concept.Practical implications:The paper articulates on the opportunities in applying KR and other AI methods and techniques to enhance the functions of KO.Originality/value:Ontologies and linked data as the evidence of the convergence of KO and KR paradigms provide theoretical and methodological support to innovate KO in the AI era.
文摘人工智能科学(AI for science,AI4S)为代表的新范式正在重塑科学研究。作为AI4S的关键技术,大语言模型在教育智能体建模与仿真、教育过程挖掘、教育数据增广等方面展现出了巨大潜力。研究立足科学哲学领域的“问题-方法-过程”框架,剖析了大语言模型引发的教育研究范式变革图景:在问题维度,大语言模型基于海量数据形成的“世界知识”拓宽了教育研究的问题视野;在方法维度,大语言模型依托其“泛思维链”能力,为情境建模、模拟仿真、因果推断等方法创新提供新的可能;在过程维度;大语言模型为“端到端”和“人在回路”理念在教育研究中的融合提供了理想的技术载体,开启了人机协同的新范式;结合教育研究范式演进的历史维度,当前AI4S引领的变革是社会需求牵引和技术进步双重驱动的必然,既延续了数字时代教育研究范式的演进逻辑,还在智能维度、生成式范式、跨界整合等方面实现了独特突破。需要指出的是,这场范式变革虽然前景广阔,但其复杂性也不容忽视。研究对教育知识生产“单一文化”、理解错觉加剧、模型黑箱效应等潜在风险作了深度探讨,提出了重塑教育研究的反思性、审慎评估大语言模型适用边界的策略实施,为应对AI4S时代的教育机遇与挑战提供了新思路。