Virtual documents are hypermedia documents that are generated on demand in response to reader input. This paper describes a virtual document application that generates natural language explanations about the structure and behavior of electromechanical systems. The application, called DME, structures the interaction with the reader as a question–answer dialog. Each page of the hyperdocument is the answer to a question, and each link on each page is a follow-up question that leads to another answer. DME is a model-based virtual document generator; unlike conventional database front-ends that provide views onto data, DME dynamically constructs the document's content (i.e. coherent explanations in English) from underlying mathematical and symbolic models. DME-based virtual documents have been running on the WWW since late 1993. They are used to document engineered systems in support of collaborative design and simulation-based training. In this paper we describe and demonstrate the DME application (with examples that run), and describe how it generates virtual documents on the web. We discuss the impact that model-based virtual documentation can have on the way technical documentation is authored and delivered.

We show how to deliver a sophisticated, yet intuitive, interactive application over the web using off-the-shelf web browsers as the interaction medium.This attracts a large user community, improves the rate of user acceptance, and avoids many of the pitfalls of software distribution.Web delivery imposes a novel set of constraints on user interface design.We outline the tradeoffs in this design space, motivate the choices necessary to deliver an application, and detail the lessons learned in the process.These issues are crucial because the growing popularity of the web guarantees that software delivery over the web will become ever more wide-spread.

In the VT/Sisyphus experiment, a set of problem solving systems were being built against a common specification of a problem. An important hypothesis was that the specification could be given, in large part, as a common ontology. This article is that ontology. This ontology is different than normal software specification documents in two fundamental ways. First, it is formal and machine readable (i.e. in the KIF/Ontolingua syntax). Second, the descriptions of the input and output of the task to be performed include domain knowledge (i.e. about elevator configuration) that characterize semantic constraints on possible solutions, rather than describing the form (data structure) of the answer. The article includes an overview of the conceptualization, excerpts from the machine-readable Ontolingua source files, and pointers to the complete ontology library available on the Internet.

Recent work in Artificial Intelligence (AI) is exploring the use of formal ontologies as a way of specifying content-specific agreements for the sharing and reuse of knowledge among software entities. We take an engineering perspective on the development of such ontologies. Formal ontologies are viewed as designed artifacts, formulated for specific purposes and evaluated against objective design criteria. We describe the role of ontologies in supporting knowledge sharing activities, and then present a set of criteria to guide the development of ontologies for these purposes. We show how these criteria are applied in case studies from the design of ontologies for engineering mathematics and bibliographic data. Selected design decisions are discussed, and alternative representation choices are evaluated against the design criteria.

A central purpose of knowledge acquisition technology is to assist with the formulation of domain models that underlie knowledge systems. In this article we examine the model formulation process itself as a problem-solving task. Drawing from AI research in qualitative reasoning about physical systems, we characterize the model formulation task in terms of the inputs, the reasoning subtasks, and the knowledge needed to perform the problem solving. We describe the elements of a high-level representation of modeling knowledge, and techniques for providing intelligent assistance to the model builder. Applying the results from engineering modeling to knowledge acquisition in general, we identify properties of the representation that facilitate the construction of knowledge systems from libraries of reusable models. © 1993 John Wiley & Sons, Inc.