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 ...

Useful and suitable action representations, with accompanying planning algorithms are crucial for the task performance of many agent systems, and thus a core issue of research on intelligent agents. An efficient and expressive representation of actions and plans can allow planning systems to retrieve relevant knowledge faster and to access and use suitable actions more effectively [18]. Two general approaches have been pursued in the past; STRIPS-based planners, which construct plans from scratch, based on primitive action descriptions and planners using pre-defined Plan Decompositions Hierarchies, also known as Hierarchical Task Networks. In our research, we integrated both an inheritance hierarchy of actions, using STRIPS-like action descriptions, with a plan decomposition hierarchy, which consists of pre-defined plan schemata. This combination is suitable for a richer action and plan representation, and thus an improved planning algorithm. We implemented and tested this approach for a prototypical example application: the travel planning domain. 1.

The development and maintenance of domain-specific application ontologies require knowledge input from domain experts who are usually without any formal ontology or AI background. When dealing with large-scale ontologies, for example of the kind with which we are currently familiar in the biomedical spheres, quality assurance becomes important in minimizing modelling mistakes and the application errors which they bring in their wake. In this paper we describe how the upper-level framework BFO (for: Basic Formal Ontology), developed by the Institute for Formal Ontology and Medical Information Science, is being used to provide automatic error detection and run-time modelling support to the development of LinKBase, a large-scale medical domain ontology developed by Language and Computing NV to serve a range of natural language processing applications.