论文标题
明显：用于数据挖掘，机器学习和通用知识管理的开发方法和知识基础拓扑
Evident: a Development Methodology and a Knowledge Base Topology for Data Mining, Machine Learning and General Knowledge Management
论文作者
论文摘要
已开发了30多年的软件用于知识发现，预测和管理。但是，使用现有的项目开发和工件管理方法时，仍然存在尚未解决的疼痛点。从历史上看，缺乏适用的方法。此外，已应用的方法（例如敏捷）有几个局限性，包括科学不可约解性，以降低其适用性。显然，提出了一种植根于逻辑推理哲学和EKB的发展方法，即知识基础拓扑。数据挖掘，机器学习和通用知识管理中的许多疼痛点都可以从概念上缓解。可以扩展显而易见的，以加速哲学探索，科学发现，教育以及全球知识共享和保留。 EKB提供了一种将信息作为知识的解决方案，即数据上面的粒度级别。还讨论了计算机历史，软件工程，数据库，传感器，哲学以及项目与组织与军事管理方面的相关主题。
Software has been developed for knowledge discovery, prediction and management for over 30 years. However, there are still unresolved pain points when using existing project development and artifact management methodologies. Historically, there has been a lack of applicable methodologies. Further, methodologies that have been applied, such as Agile, have several limitations including scientific unfalsifiability that reduce their applicability. Evident, a development methodology rooted in the philosophy of logical reasoning and EKB, a knowledge base topology, are proposed. Many pain points in data mining, machine learning and general knowledge management are alleviated conceptually. Evident can be extended potentially to accelerate philosophical exploration, science discovery, education as well as knowledge sharing & retention across the globe. EKB offers one solution of storing information as knowledge, a granular level above data. Related topics in computer history, software engineering, database, sensor, philosophy, and project & organization & military managements are also discussed.
