The use and importance of ontologies is becoming more widespread, however building ontologies is largely a black art. The aim of this paper is to identify and characterise what we currently know and to move towards the longer term goal of developing a comprehensive unified methodology. We first identify dimensions for characterising ontologies, to be used as a basis for noting which techniques and guidelines for building ontologies apply in different circumstances. We then give an overview of the current state of the art, noting that most work addresses just a small part of the life cycle. The very few more complete methods are limited to case studies involving single ontologies and they are hard to compare. In the main part of this paper, we examine two such methods and give a framework for comparing and unifying them. We emphasise that different approaches are required for difference circumstances, and give some guidelines for when to use which techniques. We conclude by ...

In conceptual design of engineering devices, a designer decomposes a required function into sub-functions, so-called functional decomposition, using a kind of functional knowledge representing achievement relations among functions. However, such knowledge about functionality of engineering devices is usually left implicit because each designer possesses it. Even if such knowledge is found in documents, it is often scattered around technical domains and lacks consistency. Aiming at capturing such functional knowledge explicitly and sharing it in design teams, we discuss its systematic description based on functional ontologies which provide common concepts for its consistent and generic description. We propose a new concept named "way of achievement" as a key concept for capturing such functional knowledge. Categorization of typical representations of the knowledge and its organization as is-a hierarchies are also discussed. The generic concepts representing functionality of a device in the functional knowledge are provided by the functional concept ontology, which makes the functional knowledge consistent and applicable to other domains. We also discuss development of a design supporting system using the systematized knowledge, called a functional way server. It helps human designers redesign an existing engineering device by providing a wide range of alternative ways of achievement of the required function in a manner suitable for the viewpoint of each designer and then facilitates innovative design.

This paper proposes a new category of functions of artifacts called meta-function. Although conventional research on functional representation defines function as some kinds of abstraction of changes in objects associated with components, a meta-function of a component represents a role played by a function of the component for another function of another component without mention of such changes specific to the target system. The meta-functions explain the types of collaboration and dependency among functions of components as part of the design rationale of the target artifacts. This paper defines nine types of the meta-functions such as ToEnable and ToDrive, which form part of an ontology of functional concepts. The utility of the ontology in explanation and redesign is also discussed.

. Knowledge-acquisition is well known to be a bottleneck activity in the development of knowledge-based systems. Several techniques and tools were proposed to support this process. However, knowledge engineers still have difficulties to understand the problem domain, to apply these techniques and to interact with the experts. Considering that domain-specific tools can be useful for knowledge acquisition, we defined a generic architecture of knowledge acquisition tools and, based on that, we built KED, a Knowledge Editor for Diagnosis in the cardiology domain. KED is part of a general environment that aims at supporting the software development in cardiology domain. 1 INTRODUCTION Knowledge acquisition is one of the longest and most difficult activities in the development of knowledge-based systems [8]. We could verify this while developing SEC [7], an expert system for diagnosis of acute myocardial infarction. At the beginning of this project the computer science team had a one-day c...