.png){kind=icon}
返回
Process factors in knowledge acquisition
.png){kind=icon}
PRE
AI
DOI：10.1111/j.1468-0394.1996.tb00283.x.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Knowledge acquisition (KA) is often characterised as a crucial bottleneck in the development of expert system applications. General definitions of KA view it as a collection of subprocesses such as elicitation, analysis and representation, but in actual practice, each KA occurrence may involve one or all of these subprocesses in varying sequences and combinations. No model or framework currently exists for describing the many possible variations in KA processes as they actually occur. This article presents a new way of characterising knowledge acquisition processes that is not tied to one particular technique or approach to KA. Three nested levels are proposed to characterise the many possible variations and combinations of KA dynamics: the process, episode and transaction levels of analysis. Each of these is further delineated in a top-down manner. Not only does this scheme provide a fairly comprehensive means for viewing KA dynamics as a whole, but it suggests several factors that have heretofore drawn little research attention. The suggested constructs capture more accurately what actually occurs in the practice of KA. In so doing they also provide the foundation for structuring future research into how KA processes may unfold.
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.3
论文数:
2.5K
被引数:
3.8K
机构
暂无机构信息
引用论文
.png){kind=icon}
A Novel Ship Detection Method in Remote Sensing Images via Effective and Efficient PP-YOLO一种基于有效高效PP-YOLO的遥感图像船舶检测新方法
PREAI
分享收藏Dual Low Identification Target Recognition in Complex Environment based on Neural Network基于神经网络的复杂环境下双低特性目标识别
PREAI
分享收藏分享收藏
查看更多内容