Developing foundations for knowledge management systems

Knowledge Management (KM) is an important issue in organizations. However there are several barriers to successful KM. In particular, knowledge hoarding, difficulties in identifying organizational knowledge, not understanding KM requirements, and technical difficulties of knowledge representation. In this work we focus on a connection between the managerial and technical aspects of knowledge management. We study the nature of organizational knowledge in order to derive knowledge management requirements to support the design of computerized Knowledge Management Systems. The work consists of three parts: 1) Defining organizational knowledge that needs to be managed. 2) Using the definition of organizational knowledge and its attributes to identify knowledge management requirements. This involves identifying the various facets of knowledge as well as the perceived meta- knowledge requirements of users. 3) Deriving guidelines for the efficient design of knowledge management systems

Evaluating End User Development as a Requirements Engineering Technique for Communicating Across Social Worlds During Systems Development

Requirements engineering is a key activity in systems development. This paper examines six systems development projects that have used end user development (EUD) as a requirements engineering technique for communicating across social worlds. For this purpose, we employed the theoretical lens of design boundary object in order to focus on functional and political ecologies during the development process. Four features were investigated: (1) the capability for common representation, (2) the capability to transform design knowledge, (3) the capability to mobilise for design action, and (4) the capability to legitimise design knowledge across social worlds. We concluded that EUD means a high degree of end user involvement and takes advantage of end users’ know-how. It has the ability to capture requirements and transfer them into the final information system without the need to make an explicit design rationale available to the systems developers. However, systems developers have little or no influence on business requirements. Their role is mainly as technical experts rather than business developers. The systems developers took control and power of technical requirements, while requirements that relate to business logic remained with the end users. Consequently, the systems developers did not act as catalysts in the systems development process

A design and implementation methodology for diagnostic systems

A methodology for design and implementation of diagnostic systems is presented. Also discussed are the advantages of embedding a diagnostic system in a host system environment. The methodology utilizes an architecture for diagnostic system development that is hierarchical and makes use of object-oriented representation techniques. Additionally, qualitative models are used to describe the host system components and their behavior. The methodology architecture includes a diagnostic engine that utilizes a combination of heuristic knowledge to control the sequence of diagnostic reasoning. The methodology provides an integrated approach to development of diagnostic system requirements that is more rigorous than standard systems engineering techniques. The advantages of using this methodology during various life cycle phases of the host systems (e.g., National Aerospace Plane (NASP)) include: the capability to analyze diagnostic instrumentation requirements during the host system design phase, a ready software architecture for implementation of diagnostics in the host system, and the opportunity to analyze instrumentation for failure coverage in safety critical host system operations

SemNet: the knowledge representation of lolita

Many systems of Knowledge Representation exist, but none were designed specifically for general purpose large scale natural language processing. This thesis introduces a set of metrics to evaluate the suitability of representations for this purpose, derived from an analysis of the problems such processing introduces. These metrics address three broad categories of question: Is the representation sufficiently expressive to perform its task? What implications has its design on the architecture of the system using it? What inefficiencies are intrinsic to its design? An evaluation of existing Knowledge Representation systems reveals that none of them satisfies the needs of general purpose large scale natural language processing. To remedy this lack, this thesis develops a new representation: SemNet. SemNet benefits not only from the detailed requirements analysis but also from insights gained from its use as the core representation of the large scale general purpose system LOLITA (Large-scale Object-based Linguistic Interactor, Translator, and Analyser). The mapping process between Natural language and representation is presented in detail, showing that the representation achieves its goals in practice

Specification of business rules for the development of hospital alarm system: application to the pharmaceutical validation.

6 pagesInternational audienceAlthough clinical alarm systems are part of the knowledge management setting within healthcare organisations, modelling of business processes related to decision support and knowledge representation of decision rules are seldom described. We propose a customization of the Unified Process that takes into account user requirements for clinical alarm systems by introducing the Semantics of Business Vocabulary and Business Rules (SBVR). This methodology was applied to the design and implementation of a clinical alarm system for pharmaceutical validation at the European Hospital Georges Pompidou (HEGP). Rules were implemented using the IlogJRules Business Rule Management System. We produced 3 business rules patterns and 427 instances of rules. As SBVR is close to natural language, pharmacists were able to understand rules and participate to their design

On semantic annotation of decision models

The growth of service sector in recent years has led to renewed research interests in the design and management of service systems. Decision support systems (DSS) play an important role in supporting this endeavor, through management of organizational resources such as models and data, thus forming the “back stage” of service systems. In this article, we identify the requirements for semantically annotating decision models and propose a model representation scheme, termed Semantically Annotated Structure Modeling Markup Language (SA-SMML) that extends Structure Modeling Markup Language (SMML) by incorporating mechanisms for linking semantic models such as ontologies that represent problem domain knowledge concepts. This model representation format is also amenable to a scalable Service-Oriented Architecture (SOA) for managing models in distributed environments. The proposed model representation technique leverages recent advances in the areas of semantic web, and semantic web services. Along with design considerations, we demonstrate the utility of this representation format with an illustrative usage scenarios with a particular emphasis on model discovery and composition in a distributed environment

DATABASE ACCESS REQUIREMENTS OF KNOWLEDGE-BASED SYSTEMS

Knowledge bases constitute the core of those Artificial Intelligence programs which have come to be known as Expert Systems. An examination of the most dominant knowledge representation schemes used in these systems reveals that a knowledge base can, and possibly should, be described at several levels using different schemes, including those traditionally used in operational databases. This chapter provides evidence that solutions to the organization and access problem for very large knowledge bases require the employment of appropriate database management methods, at least for the lowest level of description -- the facts or data. We identify the database access requirements of knowledge-based or expert systems and then present four general architectural strategies for the design of expert systems that interact with databases, together with specific recommendations for their suitability in particular situations. An implementation of the most advanced and ambitious of these strategies is then discussed in some detail.Information Systems Working Papers Serie

Powertrain Assembly Lines Automatic Configuration Using a Knowledge Based Engineering Approach

Technical knowledge and experience are intangible assets crucial for competitiveness. Knowledge is particularly important when it comes to complex design activities such as the configuration of manufacturing systems. The preliminary design of manufacturing systems relies significantly on experience of designers and engineers, lessons learned and complex sets of rules and is subject to a huge variability of inputs and outputs and involves decisions which must satisfy many competing requirements. This complicated design process is associated with high costs, long lead times and high probability of risks and reworks. It is estimated that around 20% of the designer’s time is dedicated to searching and analyzing past available knowledge, while 40% of the information required for design is identified through personally stored information. At a company level, the design of a new production line does not start from scratch. Based on the basic requirements of the customers, engineers use their own knowledge and try to recall past layout ideas searching for production line designs stored locally in their CAD systems [1]. A lot of knowledge is already stored, and has been used for a long time and evolved over time. There is a need to retrieve this knowledge and integrate it into a common and reachable framework. Knowledge Based Engineering (KBE) and knowledge representation techniques are considered to be a successful way to tackle this design problem at an industrial level. KBE is, in fact, a research field that studies methodologies and technologies for capturing and re-using product and process engineering knowledge to achieve automation of repetitive design tasks [2]. This study presents a methodology to support the configuration of powertrain assembly lines, reducing design times by introducing a best practice for production systems provider companies. The methodology is developed in a real industrial environment, within Comau S.p.A., introducing the role of a knowledge engineer. The approach includes extraction of existing technical knowledge and implementation in a knowledge-based software framework. The macro system design requirements (e.g. cycle time, production mix, etc.) are taken as input. A user driven procedure guides the designer in the definition of the macro layout-related decisions and in the selection of the equipment to be allocated within the project. The framework is then integrated with other software tools allowing the first phase design of the line including a technical description and a 2D and 3D CAD line layout. The KBE application is developed and tested on a specific powertrain assembly case study. Finally, a first validation among design engineers is presented, comparing traditional and new approach and estimating a cost-benefit analysis useful for future possible KBE implementations

Knowledge base management systems - the bases of advanced CAD

Semantic expressive representation of design objects, active system behavior combined with reasoning facilities, and efficient implementation concepts are necessary requirements for the construction of better CAD systems. Here, we describe our approach to a knowledge base management system and exemplify its usage for advanced CAD systems

Automated Problem Domain Cognition Process in Information Systems Design

An automated cognitive approach for the design of Information Systems is presented. It is supposed to be used at the very beginning of the design process, between the stages of requirements determination and analysis, including the stage of analysis. In the context of the approach used either UML or ERD notations may be used for model representation. The approach provides the opportunity of using natural language text documents as a source of knowledge for automated problem domain model generation. It also simplifies the process of modelling by assisting the human user during the whole period of working upon the model (using UML or ERD notations)