Knowledge management (KM) systems manipulate organizational knowledge by storing and redistributing corporate memories that are acquired from the organization's members. In this paper, we introduce a textual casebased reasoning (TCBR) framework for KM systems that manipulates organizational knowledge embedded in artifacts (e.g., best practices, alerts, lessons learned). The TCBR approach acquires knowledge from human users (via knowledge elicitation) and from text documents (via knowledge extraction) using template-based information extraction methods, a subset of natural language, and a domain ontology. Organizational knowledge is stored in a case base and is distributed in the context of targeted processes (i.e., within external distribution systems). The knowledge artifacts in the case base have to be translated into the format of the external distribution systems. A domain ontology supports knowledge elicitation and extraction, storage of knowledge artifacts in a case base, and artifact translation.

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Abstract. Experience-based Information Systems (EbIS) enable organizations to capture, store and reuse knowledge and experiences for continuous competence development. However, there are several shortcomings that seem to limit the usage of the stored knowledge. Focusing on technical issues, the stored experiences consist mainly of contextual knowledge provided by domain experts, while declarative and procedural knowledge is required in addition to facilitate learning for novices. Moreover, these systems do not support learning in an optimal way because they do not activate learning processes. We present an approach that enriches retrieved experiences with additional learning elements in so-called micro-didactical learning arrangements, created by a pedagogical agent based on cognitive learning goals and an instructional design model. The advantages of this approach are twofold: first, the applicability of experience packages increases by adding learning elements to the package; second, the application of the experience and the newly gained knowledge in practice deepens the learning effect. 1

This comprehensive analysis addresses the United States’ alarming lack of preparedness to respond effectively to a massive disaster as evidenced by Hurricane Katrina. First, a timeline of problematic response events during and after Hurricane Katrina orients readers to some of the specific problems encountered at different levels of government. Second, a list of the “Dirty Dozen”—12 major failures that have occurred in prior disasters, which also contributed to inadequate response during and after Hurricane Katrina— is presented. Third, this article encourages expanding psychology’s role beyond the treatment of trauma to encompass disaster planning and mitigation efforts from a broader public health perspective. Finally, areas for important interdisciplinary research in human behavior that will influence our nation’s overall preparedness for future catastrophes are identified, and ways psychologists can become personally involved beyond treating casualties are discussed.

Lessons learned systems (LLS) are systems that support a lessons learned process (LLP) to collect, verify, store, disseminate, and reuse organizational lessons. In this paper we examine technological, human, and managerial problems that contribute to the limited reuse of lessons in deployed LLS. This analysis results in the identification of a set of requirements that when met tend to improve the reuse of lessons. These requirements are mainly related to the identification and representation of a lesson's reuse components, i.e., what in a lesson is essential to promote reuse. We present and demonstrate a standardized format for lessons and a lesson elicitation tool (LET) that uses this format to collect lessons from human users and addresses some of the requirements while contributing to the satisfaction of other requirements. This tool illustrates how technological solutions can impact human and managerial problems.

This document describes KMIR, a framework which supports organizations in the successful implementation of Knowledge Management (KM). It follows the holistic approach of a KM introduction by considering technological, organizational and human aspects, as well as organizational culture. The KMIR framework provides recommendations based on Case-Based Reasoning (CBR) and Semantic Web technology. The best practice cases for a successful KM implementation are structured by the use of an ontology.

A learned lesson, in the context of a pre-defined organizational process, summarizes an experience that should be used to modify that process, under the conditions for which that lesson applies. To promote lesson reuse, many organizations employ lessons learned processes, which define how to collect, validate, store, and disseminate lessons among their personnel, typically by using a standalone retrieval tool. However, these processes are problematic: they do not address lesson reuse effectively. We demonstrate how reuse can be facilitated through a representation that highlights reuse conditions (and other features) in the context of lessons learned systems embedded in targeted decision-making processes. We describe a case-based reasoning implementation of this concept for a decision support tool and detail an example.

Experience-based approaches to software development promise to capture critical knowledge from software projects that can be used as a basis for continuous improvement of software development practices. Putting these ideas into practice in the quickly evolving discipline of software engineering has proven elusive. Techniques and tools are needed that help software practitioners apply past knowledge to current projects while engaging in knowledge creation processes. This paper outlines the experience factory and organizational learning approaches, both of which explore how experience-based approaches to software development can be used to improve software development practices.

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Many organizations employ lessons learned (LL) processes to collect, analyze, store, and distribute, validated experiential knowledge (lessons) of their members that, when reused, can substantially improve some evaluation measures of targeted organizational decision processes. Unfortunately, deployed LL systems do not facilitate lesson reuse, and thus fail to bridge the lesson distribution gap between the lesson repository and targeted decision processes (i.e., they fail to bring lessons to the attention of users when and where they are needed and applicable). Our approach for solving this problem, named monitored distribution, tightly integrates lesson distribution with these decision processes. We describe a case-based implementation of monitored distribution (ALDS) in a plan authoring tool suite (HICAP) and evaluate its utility in a simulated military planning domain. Our results show that monitoried distribution can significantly improve plan evaluation measures for this domain.