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.

This paper describes an integrated acquisition interface that includes several techniques previously developed to support users in various ways as they add new knowledge to an intelligent system. As a result of this integration, the individual techniques can take better advantage of the context in which they are invoked and provide stronger guidance to users. We describe the current implementation using examples from a travel planning domain, and demonstrate how users can add complex knowledge to the system.

Developing tools that allow non-programmers to enter knowledge has been an ongoing challenge for AI. In recent years researchers have investigated a variety of promising approaches to knowledge acquisition (KA), but they have often been driven by the needs of knowledge engineers rather than by end users. This paper reports on a series of experiments that we conducted in order to understandhow far a particular KA tool that we are developing is from meeting the needs of end users, and to collect valuable feedback to motivate our future research. This KA tool, called EMeD, exploits Interdependency Models that relate individual components of the knowledge base in order to guide users in specifying problem-solving knowledge. We describe how our experiments helped us addressseveral questions and hypotheses regarding the acquisition of problem-solving knowledge from end users and the benefits of Interdependency Models, and discuss what we learned in terms of improving not only...

Modifying knowledge-based systems is a complex activity. One of its di#culties is that several related portions of the system mighthavetobechanged in order to maintain the coherence of the system. However, it is di#cult for users to #gure out what has to be changed and how. This paper presents a novel approach for building knowledge acquisition tools that overcomes some of the limitations of current approaches. In this approach, knowledge of prototypical procedures for modifying knowledge-based systems is used to guide users in changing all related portions of a system. These procedures, whichwe call knowledge acquisition scripts #or KA Scripts#, capture how related portions of a knowledge-based system can be changed coordinately.By using KA scripts, a knowledge acquisition tool would be able to relate individual changes in di#erent parts of a system, enabling the analysis of each individual change from the perspective of the overall modi#cation. The paper also describes the ...

One very successful approach to developing knowledge acquisition tools use expectations of what the user has to add or may want to add, based on how new knowledge fits within a knowledge base that already exists. When a knowledge base is first created or undergoes significant extensions and changes, these tools cannot provide much support. This paper presents an approach to creating expectations when a new knowledge base is built, and describes a knowledge acquisition tool that we implemented using this approach that supports users in creating problem-solving knowledge. As the knowledge base grows, the knowledge acquisition tool derives more frequent and more reliable expectations that result from enforcing constraints in the knowledge representation system, looking for missing pieces of knowledge in the knowledge base, and working out incrementally the inter-dependencies among the different components of the knowledge base. Our preliminary evaluations show a thirty percent time...

To make cost-effective use of a library of reusable problem-solving methods, developers must be able to quickly find and understand these methods, so that they can match their problem and knowledge base to a method in the reuse library. This match includes both finding the method and connecting to that executable software module via a set of mappings or mediators. To enable this match, builders of a reuse library must describe each method with a method-description language. We discuss a number of general desiderata for such a language. Our work focuses on one feature of the language: a precise specification of the input requirements of the method. This specification allows developers to correctly match their knowledge base to a pre-existing problem-solving method by formalizing the requirements of methods in the library. We propose an ontology for a method-description language, and present example specifications for two well-known problem-solving methods: propose-and-revise and cover-and-differentiate.

this paper, we present a development environment that can ease the task model acquisition process. The environment combines direct model editing, machine learning based upon annotated examples, and model verification through regression testing. The learning techniques and most of the other major components of this environment are in place; however, the graphical front-end is still under development

Knowledge Acquisition #KA# Scripts capture typical modi#cation sequences that users follow when they modify knowledge bases. KA tools can use these Scripts to guide users in making these modi #cations, ensuring that they follow all the rami- #cations of the change until it is completed. This paper describes our approach to design, develop, and organize a library of KA Scripts. We report the results of three di#erent analysis to develop this library, including a detailed study of actual modi#cation scenarios in two knowledge bases. In addition to identifying a good number of KA Scripts, we found a set of useful attributes to describe and organize the KA Scripts. These attributes allow us to analyze the size of the library and generate new KA Scripts in a systematic way. Wehave implemented a portion of this library and conducted two di#erent studies to evaluate it. The result of this evaluation showeda15to52percent time savings in modifying knowledge bases and that th...

The area of knowledge acquisition research concerned with the developmentof knowledge acquisition #KA# tools is in need of a methodological approachtoevaluation. E#orts such as the Sisyphus experiments have been useful to illustrate particular approaches, but have not served in practice as testbeds for comparing and evaluating di#erent alternative approaches. This paper describes our experimental methodology to conduct studies and experiments of users modifying knowledge bases with KA tools. We also report the lessons learned from several experiments that wehave performed. Our hope is that it will help others design or improve future user evaluations of KA tools. We found that performing these experiments is particularly hard because of di#culties in controlling factors that are unrelated to the particular claims being tested. We discuss our ideas for improving our current methodology and some open issues that remain.

By dividing the world into domain knowledge and problem-solving methods that reason over that knowledge, knowledge-based systems seek to promote reusability and shareability of the given components. However, to effect a working system, the components must be mated subsequently within some global structuring scheme. We created a knowledge-based design for one such structuring scheme, for composing working applications from reusable knowledge-based components. The design is based on the concept of declarative mapping relations, which are explicit specifications for the syntactic and semantic connections between entities in the knowledge and method components. We structured the design of the mapping relation types into a principled mapping ontology. We built a functional implementation of this design within the Protg system. To assess its utility, we applied the mapping system to three evaluation studies of component-based reuse. 1. REUSABLE COMPONENTS AND MAPPINGS IN KNOWLEDGEBASED SYSTEMS Knowledge-based applications often make the distinction between declarative domain knowledge and the problem-solving methods that reason over that knowledge. This dichotomous abstraction promotes a degree of independence among the given components, and therefore offers the hope of being able to share and reuse these components. Work over many years in knowledge-based systems has resulted in large numbers of knowledge bases for many diverse domains, and of tested and refined problem-solving methods. Given a new task, there is a good chance that the relevant domain knowledge and an appropriate, implemented method already exist. The windfall would be realized if we could take this instantiated knowledge base and problem-solving method, and, with limited syntactic and semantic glue, bind t...