引用网络中的知识转移,知识缺口和知识孤岛
Eoghan Cunningham1,2, Derek Greene1,2
1School of Computer Science, University College Dublin, Dublin, Ireland.
PloS one
|August 1, 2025
概括
追踪科学知识的流动是具有挑战性的. 本研究介绍了一个网络分析框架,用于绘制跨学科整合的地图,并识别诸如可解释AI (XAI) 等领域的知识差距,帮助创新.
科学领域:
- 图书计量和网络科学应用于科学知识的演变.
背景情况:
- 科学的进步需要跨学科的知识交流,但在不断发展的领域中追踪这一点是困难的.
- 现有的方法难以捕捉知识流动的动态和多学科研究领域的整合.
研究的目的:
- 开发和演示一种新的网络分析框架,用于研究知识转移动态.
- 通过引用数据识别研究社区,他们的生命周期和知识转移模式.
- 在可解释的人工智能 (XAI) 的新兴领域中分析跨学科的整合.
主要方法:
- 在累积的,随时间变化的引用网络上使用动态社区检测.
- 确定研究领域作为与共享知识来源和成果的论文组.
- 将框架应用于可解释的人工智能 (XAI) 研究的案例研究.
主要成果:
- 在基础主题 (机器学习,统计学,心理学) 和当代XAI研究之间观察到有限的知识转移.
- 在XAI的某些应用领域内识别孤立的"知识孤岛".
- 在相关的XAI研究领域之间发现了显著的"知识差距",表明了交叉授粉的潜力.
结论:
- 拟议的框架有效地绘制了跨学科整合的地图,并确定了不断发展的科学领域的知识差距.
- 调查结果突出了在XAI和其他多学科领域改善知识综合和创新的机会.
- 该框架通过从引用数据分析知识生态系统,为文献审查,研究规划和科学政策提供了有价值的见解.
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