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Abstract: Intense interest in knowledge-acquisition research began 25 years ago, stimulated by the excitement about knowledge-based systems that emerged in the 1970s followed by the realities of the “AI Winter” that arrived in the 1980s. The knowledge-acquisition workshops that responded to this interest led to the formation of a vibrant research community that has achieved remarkable consensus on a number of issues. These viewpoints include (1) the rejection of the notion of knowledge as a commodity to be transferred from one locus to another, (2) an acceptance of the situated nature of human expertise, (3) emphasis on knowledge acquisition as the modeling of problem solving, and (4) the pursuit of reusable patterns in problem solving and in domain descriptions that can facilitate both modeling and system implementation. The Semantic Web community will benefit greatly by incorporating these perspectives in its work. [Copyright &y& Elsevier]

Abstract: Many ontology-development tools allow users to supplement frame-based representations with arbitrary logical sentences. However, few users actually take advantage of this opportunity. For example, in the Ontolingua ontology library, only 20% of the ontologies have any user-defined axioms. We believe the difficulty of composing axioms primarily accounts for the lack of axioms in these knowledge bases: Many domain experts cannot translate their thoughts into abstract and symbolic representations. We attempt to remedy the difficulties by identifying groups of axioms that manifest common patterns, creating “templates” that allow users to compose axioms by “filling in the blanks.” We studied axioms in two public ontology libraries, and derived 20 templates that cover 85% of all the user-defined axioms. We describe our methodology for identifying the templates and present examples. We constructed an interface that allows users to create constraints on knowledge bases by “filling in blanks;” our usability testing shows that users could use templates to encode axioms with a success rate similar to that of experts writing directly in an axiom language. Our approach should foster the introduction of axioms and constraints that are currently missing in many ontologies. [Copyright &y& Elsevier]

Researchers in the ontology-design field have developed the content for ontologies in many domain areas. This distributed nature of ontology development has led to a large number of ontologies covering overlapping domains. In order for these ontologies to be reused, they first need to be merged or aligned to one another. We developed a suite of tools for managing multiple ontologies. These suite provides users with a uniform framework for comparing, aligning, and merging ontologies, maintaining versions, translating between different formalisms. Two of the tools in the suite support semi-automatic ontology merging: iPrompt is an interactive ontology-merging tool that guides the user through the merging process, presenting him with suggestions for next steps and identifying inconsistencies and potential problems. AnchorPrompt uses a graph structure of ontologies to find correlation between concepts and to provide additional information for iPrompt. [Copyright &y& Elsevier]

The Prote´ge´ project has come a long way since Mark Musen first built the Prote´ge´ meta-tool 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 Prote´ge´ system has evolved into a durable, extensible platform for knowledge-based systems development and research. The current version, Prote´ge´-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 Prote´ge´ project through three 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: Prote´ge´ is a flexible, well-supported, and robust development environment. Using Prote´ge´, developers and domain experts can easily build effective knowledge-based systems, and researchers can explore ideas in a variety of knowledge-based domains. [Copyright &y& Elsevier]

With the growing popularity of large-scale collaborative ontology-engineering projects, such as the creation of the 11th revision of the International Classification of Diseases, we need new methods and insights to help project- and community-managers to cope with the constantly growing complexity of such projects. In this paper, we present a novel application of Markov chains to model sequential usage patterns that can be found in the change-logs of collaborative ontology-engineering projects. We provide a detailed presentation of the analysis process, describing all the required steps that are necessary to apply and determine the best fitting Markov chain model. Amongst others, the model and results allow us to identify structural properties and regularities as well as predict future actions based on usage sequences. We are specifically interested in determining the appropriate Markov chain orders which postulate on how many previous actions future ones depend on. To demonstrate the practical usefulness of the extracted Markov chains we conduct sequential pattern analyses on a large-scale collaborative ontology-engineering dataset, the International Classification of Diseases in its 11th revision. To further expand on the usefulness of the presented analysis, we show that the collected sequential patterns provide potentially actionable information for user-interface designers, ontology-engineering tool developers and project-managers to monitor, coordinate and dynamically adapt to the natural development processes that occur when collaboratively engineering an ontology. We hope that presented work will spur a new line of ontology-development tools, evaluation-techniques and new insights, further taking the interactive nature of the collaborative ontology-engineering process into consideration. [ABSTRACT FROM AUTHOR]