The evolution of Protégé: an environment for knowledge-based systems development

  • Authors:

  • John H. Gennari;Mark A. Musen;Ray W. Fergerson;William E. Grosso;Monica Crubézy;Henrik Eriksson;Natalya F. Noy;Samson W. Tu

  • Affiliations:

  • Biomedical and Health Informatics, University of Washington and Department of Medical Education and Biomedical Informatics, Washington School of Medicine, 1959 NE Pacific St., Box 35 72 40, Seattl ...;Stanford Medical Informatics, Stanford University;Stanford Medical Informatics, Stanford University;Hipbone Inc., San Carlos, CA;Stanford Medical Informatics, Stanford University;Department of Computer and Information Science, Linköping University, Sweden;Stanford Medical Informatics, Stanford University;Stanford Medical Informatics, Stanford University

  • Venue:
  • International Journal of Human-Computer Studies
  • Year:
  • 2003

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Abstract

The Protégé project has come a long way since Mark Musen first built the Protégé 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 Protégé system has evolved into a durable, extensible platform for knowledge-based systems development and research. The current version, Protégé-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 Protégé 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: Protégé is a flexible, well-supported, and robust development environment. Using Protégé, developers and domain experts can easily build effective knowledge-based systems, and researchers can explore ideas in a variety of knowledge-based domains.