The Protg project has come a long way since Mark Musen first built the Protg metatool for knowledge-based systems in 1987. The original tool was a small application, aimed at building knowledge-acquisition tools for a few specialized programs in medical planning. From this initial tool, the Protg system has evolved into a durable, extensible platform for knowledge-based systems development and research. The current version, Protg-2000, can be run on a variety of platforms, supports customized user-interface extensions, incorporates the Open Knowledge Base Connectivity (OKBC) knowledge model, interacts with standard storage formats such as relational databases, XML, and RDF, and has been used by hundreds of individuals and research groups. In this paper, we follow the evolution of the Protg project through 3 distinct re-implementations. We describe our overall methodology, our design decisions, and the lessons we have learned over the duration of the project.. We believe that our success is one of infrastructure: Protg is a flexible, well-supported, and robust development environment. Using Protg, developers and domain experts can easily build effective knowledge-based systems, and researchers can explore ideas in a variety of knowledge-based domains.

A new domain-independent knowledge-based inference structure is presented, specific to the task of abstracting higher-level concepts from time-stamped data. The framework includes a model of time, parameters, events, and contexts. A formal specification of a domains temporal-abstraction knowledge supports acquisition, maintenance, reuse, and sharing of that knowledge.

Problem-solving methods for knowledge-based systems establish the behavior of such systems by defining the roles in which domain knowledge is used and the ordering of inferences. Developers can compose problem-solving methods that accomplish complex application tasks from primitive, reusable methods. The key steps in this development approach are task analysis, method selection "from a library", and method configuration.

We have defined a knowledge-based framework for solving the task of creating abstract, intervalbased concepts from time-stamped clinical datathe knowledge-based temporal-abstraction (KBTA) method. The KBTA method decomposes the temporal-abstraction task into five tasks; a formal mechanism is proposed for solving each subtask. The KBTA framework emphasizes the explicit representation of the knowledge required for abstraction of time-oriented clinical data, and facilitates its acquisition, maintenance, reuse, and sharing. The RSUM system implements the KBTA method. We tested RSUM in several clinical domains in which the task of monitoring patients is prominent. In particular, we tested the KBTA framework in the domain of monitoring patients who have insulin-dependent diabetes. We acquired from a diabetes-therapy expert a diabetes-therapy temporal-abstraction knowledge base. Two diabetes-therapy experts (including the first one) created temporal abstractions relevant to the therapy-monitoring task from about 800 points of data from cases of diabetic patients. The RSUM system generated about 80% of the abstractions agreed by both experts; about 97% of the overall generated abstractions were valid. We discuss the advantages and limitations of the current architecture.

In their paper on "Using Explicit Ontologies in KBS Development", van Heijst and colleagues seem to take for granted Bylander and Chandrasekaran 's hypothesis on the strong dependence of knowledge represesentation on the nature and the inference strategy of the problem at hand, the socalled interaction problem: Representing knowledge for the purpose of solving some problem is strongly affected by the nature of the problem and the inference strategy to be applied to the problem. [Bylander and Chandrasekaran 1988] The fact that the van Heijst and colleagues don't attempt to explore in detail the arguments sustaining this hypothesis is particularly puzzling, since they admit that it contradicts one of the main assumptions of their well-known KADS approach [Schreiber et al. 1993], namely the separation between domain knowledge and problem-solving knowledge. They report two reasons brought by Bylande

. This paper provides an overview of the OCML modelling language: it illustrates the underlying philosophy, describes the main modelling constructs provided, and compares it to other modelling languages. 1. INTRODUCTION OCML 1 was originally developed in the context of the VITAL project (Shadbolt et al., 1993) to provide operational modelling capabilities for the VITAL workbench (Domingue et al., 1993). Over the years the language has undergone a number of changes and improvements and in what follows we will provide an overview of the current version of the language (v5.1), illustrate its underlying philosophy and compare it to other knowledge modelling languages. 2. LANGUAGE TENETS A number of ideas/principles have shaped the development of the OCML language. These are discussed in the following sections. 2.1. Knowledge-level modelling support. The main goal of OCML is to support knowledge-level modelling (Newell, 1982; Fensel and Van Harmelen, 1994). In practice this role impl...

PROTG-II is a suite of tools and a methodology for building knowledge-based systems and domain-specific knowledge-acquisition tools. In this paper, we show how PROTG-II can be applied to the task of providing protocol-based decision support in the domain of treating HIVinfected patients. For this task, we use a problem-solving method called episodic skeletal-plan refinement. This method is decomposable; we construct it from a set of reusable components. In addition, we build an application ontology that consists of the terms and relations in the domain, plus terms that supply method-specific knowledge requirements. From this ontology, we automatically generate a domain-specific knowledge-acquisition tool. The general goal of the PROTG-II approach is to produce systems and components that are easily maintained and reusable. This is the rationale for constructing a problem-solving method from a set of smaller-grained methods and mechanisms. This is also why our knowledge-acquisition tools are domain-specific and generated automatically from ontologies. Although our evaluation is still preliminary, for the application task of providing protocol-based decision support, we show that these goals of reusability and easy maintenance can be achieved. We discuss design decisions and the tradeoffs that have to be made in the development of the system. Keywords. Decision support; expert systems; knowledge acquisition.

Assumptions of problem-solving methods refer to necessary applicability conditions of problem-solving methods, indicating that a problem-solving method is only applicable to realize a task, if the assumptions are met. In principle, such assumptions may refer to any kind of condition involved in a problem-solving method's applicability, including its required domain knowledge. In this paper, we propose a conceptual organization for assumptions of problem-solving methods and suggest a formal language to describe them. For illustration we take examples from the Propose & Revise problem-solving method and from diagnosis.

sumes that, as with Web browsers, users will want to enhance and custom tailor the system's behavior by means of a variety of "plugins. " These plug-ins are modular pieces of program code that add new functionalities to Protg-2000 in well circumscribed ways. Developers can contribute new Protg-2000 plug-ins to a library maintained on the Internet, and can freely download new plug-ins to augment the behavior of their own knowledge-acquisition systems constructed using Protg-2000. Our approach establishes a new kind of knowledge-acquisition enterprise---one of building knowledge-acquisition--tool components that can be shared among a large community of users over the Internet. Protg-2000's modular architecture expands tremendously the kinds of systems that can be assembled to address specific knowledgeacquisition tasks, and offers the possibility that future knowledge-acquisition systems can be better tailored to the particular requirements of end users. 1. The Protg Lineage Since the

Metalevel tools can support the knowledge-engineering process by assisting developers in the design and implementation of domain-oriented knowledge-acquisition tools. The use of ontologies as a basis for automatic generation of knowledge-acquisition tools simplifies the tool-specification process by taking advantage of ontologies defined as part of the knowledge-engineering process. One of the drawbacks of this approach is that it separates ontology definition (in ontology editors) from instance editing (in knowledge-acquisition tools). Because many application tasks require ontology definition by domain experts, we have experimented with extending the Protege framework to generate ontology editors in addition to knowledgeacquisition tools for instances. We have explored di#erent approaches to ontology-editor specification in a series of prototype extensions to Protege. Here, metaclass and metaslot definitions are the basis for ontology editors, which can be embedded in know...