摘要翻译：
知识归因于人类，人类的问题解决行为具有主观性和复杂性。在今天的知识经济中，管理行为者群体所产生的知识的必要性怎么强调都不为过。这是由于行动者拥有某种程度的隐性知识，而这种隐性知识通常很难表达出来。解决问题需要一组行动者在特定背景下搜索和分享知识。在解决问题的上下文中表达的知识必须资本化，以便将来重用。本文提出了一种在解决经济智能过程中的决策问题时，允许相关和可靠行为者的知识动态资本化的方法。采用知识标注方法和时态属性来处理参与者之间通信的复杂性，并对表达的知识进行上下文化处理。本文建立了一个原型系统来演示基于该方法的协同知识管理系统的功能。通过实例进行了验证，结果表明动态资本化能够有效地验证知识，从而提高了获取知识的可靠性，可供重用。该系统可适用于各种领域
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英文标题：
《Dynamic Capitalization and Visualization Strategy in Collaborative
  Knowledge Management System for EI Process》
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作者：
Bolanle Oladejo (LORIA), Victor Odumuyiwa (LORIA), Amos David (LORIA)
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Knowledge is attributed to human whose problem-solving behavior is subjective and complex. In today's knowledge economy, the need to manage knowledge produced by a community of actors cannot be overemphasized. This is due to the fact that actors possess some level of tacit knowledge which is generally difficult to articulate. Problem-solving requires searching and sharing of knowledge among a group of actors in a particular context. Knowledge expressed within the context of a problem resolution must be capitalized for future reuse. In this paper, an approach that permits dynamic capitalization of relevant and reliable actors' knowledge in solving decision problem following Economic Intelligence process is proposed. Knowledge annotation method and temporal attributes are used for handling the complexity in the communication among actors and in contextualizing expressed knowledge. A prototype is built to demonstrate the functionalities of a collaborative Knowledge Management system based on this approach. It is tested with sample cases and the result showed that dynamic capitalization leads to knowledge validation hence increasing reliability of captured knowledge for reuse. The system can be adapted to various domains
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PDF链接：
https://arxiv.org/pdf/1012.3312