Challenges in Knowledge Management for Structuring Systems (original) (raw)
Related papers
The Application of Knowledge Management to Large Complex Systems
International Conference on the Management of Information Technology, 2006
When a complex entity comprises, for example, a set of individually complex, geographically distributed, evolving systems, the question of how to manage change to such a system-of-systems takes great significance. Not only must change be managed at the level of each component system, it must also be managed across the entire interrelated and interacting set. The solution presented in this paper is based on research that treats a class of warships in the Royal Australian Navy (RAN), along with the associated operational, production and support systems, as a system-of-systems (SoS) and examines the management of change in this complex entity. The paper describes a Knowledge Management (KM) strategy that can be used to plan, manage, coordinate and implement change across the entire lifecycle of the target system-of-systems and which may be generalisable to other systems-of-systems.
Information and Knowledge Management in Complex Systems
IFIP Advances in Information and Communication Technology, 2015
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.
Mechanisms for Structuring Knowledge-Based Systems
2007
In order to reduce the complexity of large knowledge-based systems and promote reusability, means for decomposing them to smaller chunks are required. MIKE, our knowledge engineering framework, provides three basic means for structuring which are described in this paper: different kinds of knowledge are separated at different knowledge layers, knowledge layers can be structured by modules, and knowledge within modules is expressed in terms of an object-centred data model. In addition, ideas from entity relationship model clustering are adapted and extended to facilitate the understandability of domain knowledge and support the formation of modules.
Interdisciplinary Information Sciences, 2011
The focus of our work is the elicitation of communication network systems' knowledge resources in a generic and reusable manner for providing the automated support to network management tasks. Key features of the proposed knowledge model are: ontological representation of static domain-content and management-expertise encoded as the core knowledge of distributed multi-agent architecture. Our emphasis has primarily been on the modularization of resource knowledge to facilitate its reuse in a flexible manner. To demonstrate the effectiveness of proposed scheme, we have implemented an experimental network in our laboratory, and the devised knowledge model has been deployed through multi-agent based middleware layer in the prototype system. A couple of application scenarios have been designed for testing with the prototype system. Experimental results confirm a marked reduction in the workloads of the network operator with our system providing the automated support to network management functions. Validation of the reusability/modifiability aspects of our system illustrates the flexible manipulation of knowledge fragments within diverse application contexts. We envisage our knowledge modeling approach as the first step towards the comprehensive knowledge acquisition, representation, and dissemination in the communication network management domain. automation of management functions is the detailed interpretation of network-related information and knowledge resources.
Knowledge Modeling Framework for System Engineering Projects
IFIP – The International Federation for Information Processing
System Engineering (SE) projects encompass knowledge-intensive tasks that involve extensive problem solving and decision making activities among interdisciplinary teams. Management of knowledge emerging in previous SE projects is vital for organizational process improvement. To fully exploit this intellectual capital, it must be made explicit and shared among system engineers. In this context, we propose a knowledge modelling framework for system engineering projects. Our main objective is to provide a semantic description for knowledge items created and/or used in system engineering processes in order to facilitate their reuse. The framework is based on a set of layered ontologies where entities such as domain concepts, actors, decision processes, artefacts, are interlinked to capture explicit as well as implicit engineering project knowledge. System Engineering (SE) is an interdisciplinary approach to enable the realization of successful systems. It is defined as an iterative problem solving process aiming
Organization, Management and Engineering of Knowledge
Knowledge Organization is a discipline that has its origin in the library field and was extended by new documentation and information tasks. Thought it claimes to encompass all kinds and aspects of knowledge storage and retrieval it is bound more or less to the idea to express the structure of knowledge which is behind a scientific collection of objects and their descriptions. Its aim is to facilitate the exchange between scientists and their knowledge. Knowledge Management instead deals with the elicitation, processing and diffusion of economically important information. Knowlege gets here the main notion of competitive intelligence for a limited target and community. Knowledge Engineering is the technique of making cognitive units and links machine readable and processable. It achieves its advantage over human interaction and understanding with the growth of the data bases and the speed of numerical based decisions. Though rather surprising information mining might be possible by Knowledge Engineering a qualitative or ethical inference remains nearly unsolved. If one contrasts Knowledge Organization, Knowledge Management and Knowledge Engineering to each other these knowledge disciplines get a clearer shape and their special claims, contributions and limitations have to be taken into account. On the other hand it becomes obvious that facing the typical problems and solutions of all knowledge disciplines will result in better outcome in each. Thus practical solutions will always have to take into account these three aspects of knowledge and even more.
Knowledge Modelling in Multiagent Systems: The Case of the Management of a National Network
Lecture Notes in Computer Science, 1999
This paper presents the knowledge model of a distributed decision support system, that has been designed for the management of a national network in Ukraine. It shows how advanced Artificial Intelligence techniques (multiagent systems and knowledge modelling) have been applied to solve this real-world decision support problem: on the one hand its distributed nature, implied by different loci of decision-making at the network nodes, suggested to apply a multiagent solution; on the other, due to the complexity of problemsolving for local network administration, it was useful to apply knowledge modelling techniques, in order to structure the different knowledge types and reasoning processes involved. The paper sets out from a description of our particular management problem. Subsequently, our agent model is described, pointing out the local problem-solving and coordination knowledge models. Finally, the dynamics of the approach is illustrated by an example.
Knowledge Engineering Systematisation of Technical Enterprises Memories
ISO/TC 184/SC4, Automation systems and integration, At Florence, Italy, Volume: WG3 Meetings, 20-23 Oct., 1997
The paper intends to describe the architecture of the KEYSTONE (Knowledge Engineering Systematisation of Technical Enterprises memories) project which aims to contribute to create a market based on the systematisation of engineering knowledge in its various forms. KEYSTONE will not only rely on product models, but will also address other form of engineering knowledge such as product libraries, intelligent systems including expert systems and knowledge bases, etc. These different forms of knowledge will be represented by various IT mechanisms including widely available industry specifications and standards wherever possible. Engineering knowledge goes beyond simple product data or product catalogues data and includes many other forms of dynamic information which are often required for most of the everyday engineering practice. Nevertheless, appropriate static information constructs are a prerequisite to establishing more sophisticated forms of IT representations leading to the generalisation of engineering knowledge. This is one of the reasons why investments made in STEP (ISO 10303) and Part Libraries (ISO 13584) represent a sound basis to start from to further develop and propose new forms of knowledge representation that will extend the capabilities offered now by ten years of a continuous effort.