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.

The use of predefined models of problem-solving methods is receiving considerable attention from researchers in the area of knowledge acquisition. Using these models, developers of knowledge-acquisition tools are able to prescribe the roles in which knowledge is used in completing a given task. A number of method-oriented architectures based on a single problem-solving method have been developed by various research groups. Because the methods are domain-independent, method-oriented architectures are limited by the fact that knowledge roles that depend on domain-specific considerations cannot be represented using the model of problem solving. In addition, the interface between the knowledge-acquisition tool and the application expert cannot adequately convey the role of each knowledge type in the task model. PROTG-II is a knowledge-acquisition shell that we are building to generate knowledge-acquisition tools automatically without presupposing a specific model of problem-solving. The shell manages a library of mechanisms---procedures of grain size smaller than that of problem-solving methods. Mechanisms can be combined in PROTG-II to construct problem-solving methods and to define the roles of knowledge that depend on domain considerations. Furthermore, PROTG-II utilizes the concept of adaptation in interfaces to allow the knowledge engineer to produce interfaces that are task- and domain-specific. In this paper, we present the PROTG-II shell and examine the components of its architecture. We also demonstrate the use of PROTG-II with a running example, and discuss the design techniques used to overcome the limitations of method-specific architectures.

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.

The knowledge acquisition bottleneck impeding the development of expert systems is being alleviated by the development of computer-based knowledge acquisition tools. These work directly with experts to elicit knowledge, and structure it appropriately to operate as a decision support tool within an expert system. However, the elicitation of expert knowledge and its effective transfer to a useful knowledge-based system is complex and involves a diversity of activities. This paper illustrates the complete development of a decision support system using knowledge acquisition tools. The example is simple enough to be completely analyzed but exhibits enough real-world characteristics to give significant insights into the processes and problems of knowledge engineering. 1 Introduction Knowledge acquisition for expert system development has come to be termed knowledge engineering, following Feigenbaum's (1980) use of the term to describe the reduction of a large body of knowledge to a precise...

This paper describes how we applied the PROTG-II architecture to build a knowledgebased system that configures elevators. The elevator-configuration task was solved originally with a system that employed the propose-and-revise problem-solving method (VT; Marcus, Stout & McDermott, 1988). A variant of this task, here named the Sisyphus-2 problem, is used by the knowledge-acquisition community for comparative studies. PROTG-II is a knowledge-engineering environment that focuses on the use of reusable ontologies and problem-solving methods to generate task-specific knowledge-acquisition tools and executable problem solvers. The main goal of this paper is to describe in detail how we used PROTG-II to model the elevator-configuration task. This description provides a starting point for comparison with other frameworks that use abstract problem-solving methods. Starting from a detailed description of the elevator-configuration knowledge (Yost, 1992), we analyzed the domain knowledge and developed a general, reusable domain ontology. We selected, from PROTG-II's library of preexisting methods, a propose-and-revise method based on chronological backtracking. We then configured this method to solve the elevator-configuration task in a knowledge-based system named ELVIS. We entered domain-specific knowledge about elevator configuration into the knowledge base with the help of a task-specific knowledge-acquisition tool that was generated from the ontologies. After we constructed mapping relations to connect the domain and method ontologies, PROTG-II generated the executable problem solver. We have found that the development of ELVIS has provided a valuable test case for evaluating PROTG-II's suite of system-building tools.

PROTEGE is a metalevel program that generates knowledge-acquisition tools that are based on the method of skeletal-plan refinement. In this paper, we propose a flexible and extensible architecture that allows the problem-solving method to be assembled from more basic methods. In this architecture, we emphasize (1) a uniform view of problem solving at different levels of granularity, (2) an explicit data model that allows construction of complex datatypes from predefined datatypes, and (3) the inclusion of domaindependent control information within a domain-independent problem-solving method. We show how such a model of problem solving can drive the generation of knowledge-acquisition tools.

. Although skeletal plan refinement is used in several planning systems, a procedure for the automatic acquisition of such high-level plans has not yet been developed. The proposed explanation-based knowledge acquisition procedure constructs a skeletal plan automatically from a sophisticated concrete planning case. The classification of that case into a well-described class of problems serves as an instrument for adjusting the applicability of the acquired skeletal plans to that class. The four phases of the proposed procedure are constituted as follows: In the first phase, the execution of the source plan is simulated, and explanations for the effects of the occurred operators are constructed. In the second phase, the generalization of these explanations is performed with respect to a criterion of operationality which specifies the vocabulary for defining abstract operators for the skeletal plan. The third phase, a dependency analysis of the resulting operator effects, unveils the int...

Domain-oriented knowledge-acquisition tools provide efficient support for the design of knowledge-based systems. However, the cost of developing such tools is high, especially when their restricted scope is taken into account.

This article describes a case study in which Prot eg e-2000 was used to build a tool for constructing CommonKADS knowledge models. The case study tries to capitalize on the strong points of both approaches in the tool-support and modeling areas. We specify the CommonKADS knowledge model as an ontology in the Prot eg e specification formalism, and define a number of visualizations for the resulting types. The study shows that this type of usage of Prot eg e-2000 as a "metaCASE" tool is to a large extent feasible. In particular, the flexible class/instance distinction in Prot eg e is a feature that is needed for undertaking such a metamodeling exercise. The case study revealed a number of problems, such as the representation of rule types. The study also led to a set of new tool requirements, such as extended expressivity of the Prot eg e forms. Finally, this experience shows how the concrete, operational approach of Prot eg e and the highly methodological approach of CommonKADS can be combined successfully to provide the middle-ground tool that reduces the gap between a conceptual model and a usable knowledge model.

Abstract. This paper describes Expedition, an environment designed to facilitate the quick ramp-up of MT systems from practically any alphabetic language (L) into English. The central component of Expedition is a knowledge elicitation system that guides a linguistically naive bilingual speaker through the process of describing L in terms of its ecological, morphological, grammatical, lexical, and transfer information. Expedition also includes a module for converting the elicited information into the format expected by the underlying MT system and an MT engine that relies on both the elicited knowledge and resident knowledge about English. The Expedition environment is integrated using a configuration and control system. Expedition represents an innovative approach to answering the need for rapid-configuration MT by preparing an MT system in which the only missing link is information about L, which is elicited in a structured fashion such that it can be directly exploited by the system. In this paper we report on the current state of Expedition with an emphasis on the knowledge elicitation system. 1.