Attentions in knowledge engineering and management
Knowledge has been recognized as a source of wealth of the human society and as an important asset for economies, industries, enterprises, teams, and individuals. On the one hand, knowledge helps us to decouple economic growth from consumption of materials and energy, and to follow sustainable production policies. On the other hand, the knowledge embedded in products and processes makes it possible to generate more added values, and to increase the service components related to products. One of the hottest issues in the society today is knowledge-intensive performance support that concerns industry, business, research, education, technology, and services. In order to achieve and maintain a competitive edge in product realization, companies must follow the fast development of knowledge technologies, and bring inventive knowledge to a first-tier position. Knowledge helps us, for instance, cope with the challenges of globalization of industrial operations, to handle the growing functional and structural complexities of artefacts, and to customize products according to the need of individual customers. Leading companies are developing into virtual enterprises that have the potential to capitalize on the best utilization of all kinds of directly or indirectly available knowledge.
Knowledge management has been defined as the discipline and set of activities of identifying all particular intellectual assets within industrial organizations, processing them in computer-based systems, and disposing them in ways that enable communities and individuals to identify, generate, qualify, share, integrate, store, process, and deploy knowledge vital to the problems of an enterprise on line. Manifestation of knowledge has multiple dimensions. In industrial operations, it is typically characterized as: (i) a product (e.g. knowledge base, product documents) and a process (e.g. brainstorming, best practices guideline), (ii) formal knowledge (applied science knowledge, standards and codes) and informal knowledge (experiences, know how), (iii) macro knowledge (used on an enterprise level) and micro knowledge (used on a task level), and (iv) generic knowledge (independent from concrete products and processes) and specific knowledge (depending on particular products and processes). The main challenge for knowledge engineering originates in this complexity. Implicit knowledge has to be transferred into the stage of codified explicit knowledge by the process of de-contextualization, which needs a shared context in which knowledge is created, integrated, and utilized.
Knowledge asset management should disclose the knowledge required for solving complex product development problems by: (i) sharing the knowledge of the individual designers and engineers, (ii) brokering the existing and unbounded mental capacities, (iii) eliciting and formalizing knowledge for processing by knowledge-intensive systems and intelligent agents, and (iv) archiving knowledge assets in warehouses for contemporary or future uses. As a matter of fact, the dual nature of knowledge (i.e. its manifestation as a product and as a process) is disappearing when it is handled as an asset. Knowledge asset management is not a trivial task since knowledge: (i) spreads over all domains of enterprise operations, (ii) is usually functionally segregated according to products and processes, (iii) can appear explicitly and implicitly, and (iv) involves both formal and tacit forms such as experience, know how, skill, expertise, and competency. Therefore, knowledge asset management and warehousing must extend not only to exploration, formalization, structuring, validation, and archiving knowledge, but also to educate designers and engineers on the creation, sharing, integration, and use of explicit and implicit knowledge under rapidly changing circumstances. The development and application of knowledge intensive systems and intelligent agents are stimulated by the need to reduce the incurred labour costs through the application of smart problem-solvers or software tools and to support solving the product development and business management tasks of the enterprise when human experts are sparse.
Based on this introduction it can be concluded that knowledge engineering and management, and knowledge asset management in particular, are complex tasks, facing many challenges, and bringing in a myriad of issues and problems. At the same time considerable progress has been achieved and efforts are being made towards new approaches and solutions. The intent of the editors of this special issue was to present the current state of the art through a collection of research and application papers. As a basis, they relied on the papers that had been presented at the Fourth International Symposium on Tools and Methods of Competitive Engineering (TMCE 2002), held in Wuhan, China, 22–26 April 2002. The papers have been selected based on their contribution to theories, methodologies and tools of advanced knowledge engineering and management, knowledge-intensive technologies, and to knowledge-centred technological innovation for companies. In selecting the papers for this special issue, the following criteria have been considered: (i) technical content and originality, (ii) scientific contribution and industrial significance, (iii) advancement in or opportunities offered by the research work, (vi) perspective of application, and (v) comprehensiveness of reporting. In order to achieve a high standard, all papers have been peer reviewed and revised for the Journal of Engineering Design.
Entitled ‘Knowledge management in engineering design: personalization and codification’, the first paper by Chris McMahon, Alistair Lowe, and Steve Culley studies the exploitation of intellectual assets of distributed organizations. This is actually a survey paper that gives an overview of the current status and the open issues. Being one of the key enabling technologies for distributed enterprises, knowledge management begs for dedicated technologies as well as a better understanding of knowledge. The authors differentiate personalization approaches that emphasize human resources and communication, and codification approaches that emphasize the collection and organization of knowledge. They conclude that no single solution could address the knowledge management needs of an entire organization and that small and large workgroups require different knowledge management systems. Development of strong communities is important in knowledge management, but it is not sufficient. Also, computer systems and communication technology play an important role.
Yoshinobu Kitamura and Riichiro Mizoguchi wrote the second paper, ‘Ontology-based systematization of functional knowledge’. They applied ontological engineering to represent functional concepts in combination with extended device ontology. First, an overview on the state of the art of ontological engineering is given. Then the authors present their proposal for systematization of functional knowledge used for synthesis. They show that functional concept ontologies guide conceptualization of artefacts from a functional point of view and facilitate building models of artefacts in the mechanical domain in a consistent way. The idea of ‘way of function achievement’ plays a key role in the proposed systematization of functional knowledge. The authors’ opinion is that ontological engineering will become more important in the coming years in systematizing engineering knowledge. Emphasis has to be placed not only on the form-related knowledge, but also on the functional and behavioural knowledge in artefact modelling and simulations.
Co-authored by Sebastian Leibrecht, Tri Ngoc Pham Van, and Reiner Anderl, the fourth paper, ‘Techniques for the integration of expert knowledge into the development of environmentally sound products’, deals with knowledge from the aspect of sustainability throughout the entire product lifecycle. The authors claim that the development of environmentally sound products requires new paradigms such as prospective sustainability analysis based on an object-oriented product development environment. The main topics of this contribution are: (i) compilation of expert knowledge about processes and emissions of the product lifecycle, (ii) adaptation to boundary conditions, and (iii) implementation in product development tools. The authors introduce an object-oriented information model, which is of a layered architecture and is a composition of a core model, multiple partial models and the inventory data. They conclude that it is theoretically possible to consider every process in the ecological assessment, and that most of the observed problems emerged due to the high degree of detail that the approach claims.
Entitled ‘Knowledge-based support of rapid product development’, the paper by Dieter Roller, Oliver Eck, and Stavros Dalakakis deals with the issues of distributed information management and the integration of interdisciplinary knowledge in rapid product development processes. The authors propose a solution, called the Active Semantic Network (ASN), for a common knowledge base that represents all information relevant in rapid product development, helps to establish a common understanding, and supports the communication and cooperation of design teams. The ASN offers new knowledge-based mechanisms to extend conventional database functionalities towards the particular requirements of modern cooperative product design. The features of ASN are situation detection, semantic integrity enforcement, concurrency control, cooperative transaction, rule-based adaptation, collaboration support, and storage management. The first experiments explored that it is important to reduce the representation structures and inference facilities to a minimum to gain an acceptable performance with a larger amount of data.
The paper by Samir Mesihovic, Johan Malmqvist, and Peter Pikosz is titled ‘Product data management system-based support for engineering project management’. The paper studies how product data management (PDM) systems and project management information systems should be used to support complex projects. PDM systems can be advantageously used for product information structuring, information access, automating workflow management, and product lifecycle management. Project management information systems can be used for high-level project scheduling, project visualization, budgeting, and reporting to management. They found that PDM systems do not support a total product information model yet, and that distributed engineering work poses a number of challenges such as information security, network performance, support for standards, and distributed working practices.
The final paper, written by Gaetano Cascini and Paolo Rissone, is ‘Plastics design: integrating TRIZ creativity and semantic knowledge portals’. The goal of the authors is to show that the combined application of advanced design tools and creativity management techniques can enhance the capability of developing innovative products. The discussed problem is redesign of the structural parts of a motor-scooter wheel from metals to polymers in which inventive problem-solving was applied in the conceptual phase and a semantic knowledge portal in the detailed design phase. It is explained how the authors developed the structured knowledge portal dedicated to design with plastics, which is offered to small and medium enterprises.
The editors would like to thank all authors for contributing to this special issue with valuable papers. They are also grateful to the reviewers for their efforts and constructive comments.