General Knowledge Management Research Articles

Building Research Administration Applications for the Academic Health Center: A Case Study

The academic health center information environment is saturated with information of varying quality and overwhelming quantity. The most significant challenge is transforming data and information into knowledge. The University of Cincinnati Medical Center's (UCMC) focus is to develop an information architecture comprising data structures, Web services, and user interfaces that enable individuals to manage the information overload so that they can create new knowledge. UCMC has accomplished much of what is reported in this article with the help of a four-year Integrated Advanced Information Management Systems (IAIMS) operation grant awarded by the National Library of Medicine in 2003. In the UCMC vision for knowledge management, individuals have reliable, secure access to information that is filtered, organized, and highly relevant for specific tasks and personal needs. Current applications and tool sets will evolve to become the next generation knowledge management applications or smart digital services. When smart digital services are implemented, silo applications will disappear. A major focus of UCMC's IAIMS grant is research administration. Testing and building out existing and new research administration applications and digital services is underway. The authors review UCMC's progress and results in developing a software architecture, tools, and services for research administration. Included are sections on the evolution to full integration, the impact of the work at UCMC to date, lessons learned during this research and development process, and future plans and needs.

Next generation knowledge management

Despite its explosive growth over the last decade, the Web remains essentially a tool to allow humans to access information. The next generation of the Web, dubbed the ‘Semantic Web’, will extend the Web’s capability through the increased availability machine-processable information. These machine-processable descriptions of Web information resources are called meta-data and are associated with ontologies, or conceptualisations of the domain of application. Meta-data and associated ontologies then allows more intelligent software systems to be written, automating the analysis and exploitation of Web-based information. This paper describes how knowledge management can be improved through the adoption of Semantic Web technology. To realise this, a number of different technologies need to be brought together. Their fusion provides the infrastructure which makes semantic knowledge management possible. Specifically, the paper discusses the use of knowledge discovery and human language technology to (semi-)automatically derive the required ontologies and meta-data, along with a methodology to support this process. We describe techniques for management and controlled evolution of ontologies and a set of semantic knowledge access tools for enhanced information access. Finally, a set of application scenarios for the technology are sketched.

Information content management with shared ontologies—at corporate research centre of EADS

The role of an industrial research centre is to elaborate and transfer new knowledge to the operational units. In this context, the researcher as an individual as well as acting in a team, needs a support for better management of information and knowledge during the work process. An analysis shows problems concerning the exploitation of individual practices, which are based on the management and the structuring of shared information by a collective. This analysis led to the elaboration of a general knowledge management model for an industrial research centre. For the development of this model we used the CIMOSA framework model. With the creation of the general knowledge management model for industrial research centres we have a framework for the analysis of needs and existing tools. The analysis framework supported the proposition of a new prototype called ANITA (ANnotation tool for Industrial TeAms) for better management and sharing of written information content for research teams.

H-TechSight — A Next Generation Knowledge Management Platform

A new breed of Knowledge Management Systems (KMS), referred to as the Next Generation of Knowledge Management (NGKM), aims at overcoming the limitations of existing KMS regarding the range and quality of supportable Knowledge Management activities. Therefore, AI-based technologies are employed to enable automated knowledge item processing instead of the information retrieval functionality provided by conventional KMS. This paper presents the Knowledge Management Platform (KMP) developed in the h-TechSight project as an NGKM solution. It is designed to support organizations in technology intensive industries in monitoring changes in their external environment as an important factor in competitiveness. Therefore, the KMP combines NGKM enabling technologies for automatically observation of information resources on the Internet in order to notify users whenever a change in their domain of interest occurs. The aim of this paper is to position the KMP as a KMS of the next generation and to delineate the lessons learned in KMS design in the h-TechSight project. Thus, we illustrate the idea of NGKM with special emphasis on enabling technologies and, on this basis, expose the functional design and technical realization of the h-TechSight KMP.

Complex Acts of Knowing: Paradox and Descriptive Self‐Awareness

We are reaching the end of the second generation of knowledge management, with its focus on tacit-explicit knowledge conversion. Triggered by the SECI model of Nonaka, it replaced a first generation focus on timely information provision for decision support and in support of BPR initiatives. Like BPR it has substantially failed to deliver on its promised benefits. The third generation requires the clear separation of context, narrative and content management and challenges the orthodoxy of scientific management. Complex adaptive systems theory is used to create a sense-making model that utilises self-organising capabilities of the informal communities and identifies a natural flow model of knowledge creation, disruption and utilisation. However the argument from nature of many complexity thinkers is rejected given the human capability to create order and predictability through collective and individual acts of freewill. Knowledge is seen paradoxically, as both a thing and a flow requiring diverse management approaches. The Cynefin Centre Membership of the Centre, which focuses on action research in organisational complexity is open to individuals and to organisations. It focuses on high-participation action research projects seeking new insights into the nature of organisations and markets using models derived from sciences that recognise the inherent uncertainties of systems comprised of interacting agents. However, the Centre is not about attempting to apply physical or biological models to organisations wholesale without attention to the uniquely human capacities of free will, awareness and social responsibility. It is about engaging human organisational complexity in its many manifestations, including the ancient collective and emergent patterns of narrative, ritual, negotiation of identity and truth, self-representation and knowledge exchange. The Centre is not about consultants or academics conducting multiple interviews or observations and deriving static hypothesises and models based on their outside "expertise". It is about creating focused dynamic interactions between traditional and unexpected sources of knowledge to enable the emergence of new meaning and insight. The Centre is based on a model of networked intelligence, creating a broad and loosely structured coalition of academics, industrial and governmental organisations to create new insight and understanding for its members into the complexity of managing in a new age of uncertainty. The basis of all Centre programmes is to look at any issue from multiple new perspectives and to facilitate problem solving through multiple interactions among programme participants. Programmes run on a national, international and regional basis and range from investigation of seemingly impossible or intractable problems to pragmatic early entry into new methods and tools such as narrative databases, social network stimulation and asymmetric threat response.

KNOWLEDGE MANAGEMENT ROUTINES FOR INNOVATION PROJECTS: DEVELOPING A HIERARCHICAL PROCESS MODEL

In this paper we use the literature on knowledge management and innovation, together with empirical data, to develop a process model for knowledge management routines in the context of innovation projects. First we develop a high-level conceptualisation from the literature, the model characterising knowledge management as consisting of three distinct phases: Discovery, Realisation and Nurture. We then expand this three phase perspective into a model of generic knowledge management routines, reporting four contrasting, exemplar cases from a wide ranging study across business sectors. Using the notions of radical and incremental innovation in both products and processes we illustrate how the three high-level phases may be further expanded into a more detailed conceptualisation of the knowledge management process. This comprises eight generic routines: Search, Capture, Articulate, Contextualise, Apply, Evaluate, Support and Re-innovate. We derive a new description of knowledge management and discuss the practical implications of the model, including the opportunities which exist for cross-sector learning between organisations which are superficially dissimilar. We conclude that the successful management of organisational knowledge in the context of innovation requires attention to be paid to all eight generic routines and to the influence of enablers and blockers operating both inside and outside the framework of routines.

A set of frameworks to aid the project manager in conceptualizing and implementing knowledge management initiatives

Over the years, researchers and practitioners have been concerned about the “collection” of information and knowledge. Now with Web-based and Intranet technologies, we have the “connectivity” to allow information and knowledge sharing to take place. In recent years, the term “knowledge management” has been proposed, and numerous individuals and organizations are trying to put more “science” behind the “art” of knowledge management. To help in this direction, this paper addresses some useful frameworks to help project managers and others in conceptualizing and implementing knowledge management initiatives. A generic knowledge management implementation framework is proposed. This paper should provide the building blocks necessary to further understand and develop knowledge management initiatives.

Complex acts of knowing: paradox and descriptive self‐awareness

We are reaching the end of the second generation of knowledge management, with its focus on tacit‐explicit knowledge conversion. Triggered by the SECI model of Nonaka, it replaced a first generation focus on timely information provision for decision support and in support of BPR initiatives. Like BPR it has substantially failed to deliver on its promised benefits. The third generation requires the clear separation of context, narrative and content management and challenges the orthodoxy of scientific management. Complex adaptive systems theory is used to create a sense‐making model that utilises self‐organising capabilities of the informal communities and identifies a natural flow model of knowledge creation, disruption and utilisation. However, the argument from nature of many complexity thinkers is rejected given the human capability to create order and predictability through collective and individual acts of freewill. Knowledge is seen paradoxically, as both a thing and a flow requiring diverse management approaches.

The Design and Implementation of a Computer-Assisted Learning Expert System

As Internet usage becomes more popular over the world, e-learning system, e.g., online learning, employee training courses, e-books, etc. has been accepted globally. How to provide the optimum content for individualized learning becomes an important issue for e-learning system. In this work, a Computer-Assisted Learning Expert System (CAL-ES), based upon expert system methodology, is proposed to provide individualized teaching materials for learners in accordance with their learning aptitudes and evaluation results. According to the cycle of knowledge management in CAL system, which is similar to the cycle of general knowledge management, four mechanisms, i.e. Knowledge Representation, Knowledge Acquisition, Knowledge Organizer, and Knowledge Miner, are designed in CAL-ES. The Object Oriented Course Model (OOCM) is used as the presentation of knowledge in designing CAL system, and is used in CAL-ES. Knowledge Acquisition technology is applied to acquire knowledge from experts, including the knowledge for constructing Teaching Object, building Teaching Materials, and managing Teaching Resources. Knowledge Organizer is a directory service for efficiently managing teaching resources. Finally, Knowledge Miner is used to find implicit knowledge from historical records of the system using Data Mining technologies. These four mechanisms in CAL-ES provide complete knowledge system capability for a CAL system. In the last three years, we have been working on building an integrated CAL platform in the Virtual Mathematical High School project, and the prototype of CAL-ES will be introduced in this work.

Knowledge management and drug development

The paper is aimed at two distinct readerships: drug industry participants who are interested in how knowledge management applies to their industry and knowledge management workers who are interested in seeing how general knowledge management issues play out in this problem domain. To accommodate the latter, without boring the former, a lengthy sidebar offers an introduction to drug development and key vocabulary. We describe key business and operational issues in knowledge management for drug development, show how knowledge management is an appropriate technology for modern drug development, argue for the importance of domain models, and make connections to representative theoretical and experimental work.

An Advanced Case-Knowledge Architecture Based on Fuzzy Objects

The case-based reasoning (CBR) architecture described in this paper represents a substantive advancement in the representation of case-knowledge. It addresses three major problems found in current CBR schemes: 1) Insufficient treatment of abstract case features‘ context-dependent characteristics. 2) Lack of a methodical support for atomic and structured case features that contain and represent imprecisely specified quantities. 3) And little account for clustering and organising cognate cases into conceptually overlapping categories. To overcome the representational inadequacy resulting from those deficiencies, this work proposes two modelling fundamentals, namely, fuzzy primitive and fuzzy complex abstract features. These allow a flexible, polymorphic encoding of case characteristics as real numbers, linguistic terms, fuzzy numbers and fuzzy complex objects respectively. Based on this concept, it is possible to systematically organise a case base in fuzzy categories, reflecting real-world case clusters. In the presented scheme, a prototype case and its associated approximation scales form the basis to realise a versatile mechanism to represent the context-specific idiosyncrasies of fuzzy abstract case features. Case categories, fuzzy abstract features, cases, and the approximation scale concept are modelled as self-contained, operational entities. They co-operatively concert their services when they categorise an unclassified problem description (target case), and locate relevant stored cases. Applied to the Coronary Heart Disease risk assessment domain, the proposed architecture has proven to be highly adequate for capturing and efficiently processing case-knowledge. Moreover, as this scheme is designed upon well-established object-oriented principles, it has been shown that it can seamlessly integrate in a wider, more general knowledge management regime.

A multi-functional knowledge management system

We describe a general purpose knowledge management system, discussing its general goals and features, as well as its use in several very different applications. By "multi-functional", we mean having a wide variety of knowledge management functions such as debugging, formatting, and retrieval, and a wide variety of possible applications. The system, called CODE, functions primarily as a "knowledge engineer's rapid prototyper", or as a "spreadsheet for ideas"; one can experiment rapidly with relationships between concepts and obtain quick feedback on the desirability of changes and additions to a knowledge base. CODE's highly graphic interface permits experimentation with descriptions or definitions of concepts, which are arranged in an inheritance network using a very flexible inheritance mechanism. Several associated subsystems, such as a first order logic system and a simple natural language system, allow various types of syntactic and semantic checks to be performed if desired. We illustrate CODE's flexibility by describing three typical applications: in software engineering, terminology, and ontological design for knowledge-based systems.