1. Protégé/2000 Advanced Tools for Building Intelligent Systems Mark A. Musen Stanford University Stanford, California USA

2. Generations of Protégé systems at SMI n PROTÉGÉ LISP-Machine system for rapid knowledge acquisition for clinical-trial specifications n PROTÉGÉ-II NeXTSTEP system that allowed independent ontology editing and selection of alternative problem-solving methods n Protégé/Win Finally, a Protégé system for the masses... n Protégé/Java The subject of this talk...

3. Protégé/2000 n Represents the latest in a series of interactive tools for knowledge-system development n Facilitates construction of knowledge bases in a principled fashion from reusable components n Allows a variety of “plug ins” to facilitate customization in various dimensions n Still needs a better name...

4. Knowledge-base development with Protégé/2000 ¶ Build a domain ontology (a conceptual model of the application area) Ë Custom-tailor GUI for acquisition of content knowledge Ì Elicit content knowledge from application specialists Í Map domain ontology to appropriate problem solvers for automation of particular tasks

5. Ontologies are cropping up everywhere! n Structured online indexing (as in Yahoo!) n Mediation among heterogeneous databases (as in UMLS) n Reference characterization of application domains (for knowledge reuse)

6. An ontology n Provides a domain of discourse for characterizing some application area n Enumerates concepts, attributes of concepts, and relationships among concepts, thus defining a structure for the application area n Defines constraints on relationships among concepts

8. Conceptual building blocks for intelligent systems n Domain ontologies ä Characterization of concepts and relationships in an application area, providing a domain of discourse n Problem-solving methods ä Abstract algorithms for achieving solutions to stereotypical tasks (e.g., constraint satisfaction, classification, planning, Bayesian inference)

9. Problem-solving knowledge automates specific tasks Domain knowledge + Problem-solving method Intelligent behavior

10. Heuristic classification in MYCIN (after Clancey) WBC < 2.5 Leukopenia Immuno- suppressed Compromised host Feature Abstraction Solution Refinement Gram-negative infection Pseudo- monas E. coli Alcoholic Heuristic Match

11. Problem-solving methods n Provide terms and relationships for talking about relevant problem-solving behavior (a method ontology ) n Have a method ontology that defines knowledge roles, which clarify purpose of each element of domain knowledge n Assume that required problem solving can be construed as compositions of well-characterized, generic algorithms

12. Advances in Protégé/2000 n Much improved ä editing of ontologies ä creation and customization of knowledge- acquisition tools ä adaptation of system to new requirements n But still no automated support for mapping of knowledge bases to problem-solving methods—yet! n No more shuffling among different development tools!

13. Generation of usable domain-specific KA tools n Protégé/2000 system ä takes as input a domain ontology ä generates in real time a graphical KA tool n Developers ä Tweak KA tool appearance by using direct-manipulation layout-editing facilities ä Add custom user-interface widgets when complexity of domain warrants more specialized visual metaphors

16. Protégé/2000 Ontology-editing tab Classification problems become viewable Classification problems become viewable Developer can see knowledge organization clearly Developer can see knowledge organization clearly Easy to edit attributes and facets Easy to edit attributes and facets Add additional constraints on classes and attributes

17. A sample application tab: Authoring eligibility criteria n Developed for NCI effort to ease information management for clinical trials n Uses tab “plug in” to support custom- tailored, knowledge-driven application n Demonstrates how our ontology- oriented approach facilitates knowledge reuse and knowledge maintenance

18. The task: Authoring eligibility criteria for clinical-trial protocols n Each clinical trial protocol includes a long list of patient criteria that determine eligibility or exclusion n For given types of tumors, certain eligibility criteria can be inferred from well-characterized clinical states n Each eligibility criterion must be linked to standard patient data that can be used to determine the value of that criterion

22. Linking eligibility criteria to “Common Data Elements”

27. EligibilityWriter knowledge base n Contains clinical state information and eligibility criteria for ä Breast cancer ä Prostate cancer ä Non-small cell lung cancer n Demonstrates that entry of knowledge for each new type of tumor benefits from previously entered knowledge

28. The race to develop plug-ins n GUI widgets for ä tables ä diagrams ä animation n File I/O plug ins for interoperability with databases, other knowledge-based systems n Tab plug-ins for embedded applications

29. A Protégé-2000 KA tool for entering rules for monitoring nuclear power plants

30. Elements of Protégé-2000 Slots as first-class objects Slots as first-class objects Classes and class hierarchy Classes and class hierarchy Facets standard and user-defined Facets standard and user-defined Instances Customizable instance forms Customizable instance forms Easy browsing Easy browsing Means to view large data sets Means to view large data sets Custom widgets Custom widgets Domain- specific tabs Domain- specific tabs Components for building knowledge-based applications Components for building knowledge-based applications

31. Swapping components Each of the Protégé-2000 major components can be swapped out and replaced with a different one Knowledge model Storage model User interface

32. Open Knowledge Base Connectivity (OKBC) n Standard mechanism to access knowledge bases stored as “frames” of classes and attributes n Adopted by several well-known knowledge-representation systems (Ontolingua, LOOM) n Will allow Protégé/2000 to be used as an ontology- and knowledge-editing system for any OKBC-compliant server

33. Knowledge-base development with Protégé/2000 ¶ Build a domain ontology (a conceptual model of the application area) Ë Custom-tailor GUI for acquisition of content knowledge Ì Elicit content knowledge from application specialists Í Map domain ontology to appropriate problem solvers for automation of particular tasks

34. Protégé-2000 n Designed to use OKBC as a universal front end to a wide variety of distributed knowledge-representation systems n Generates rapidly highly customizable, domain-specific KA and knowledge maintenance tools for use directly by application specialists n Offers access to KA tools and associated application systems as Java applets n Is a set of components that developers can easily embed within other applications

35. Current work on Protégé/2000 n Enhanced GUI facilities for knowledge acquisition and knowledge browsing n Access to controlled terminologies via Lexical Technology’s Metaphrase API n Axiom language for defining constraints among instances n Plug ins for OKBC and relational- database connectivity

36. Fourth international Protégé users group meeting n July 29 through August 1 n Linköping, Sweden n Hosted by the University of Linköping n See http://www.ida.liu.se/~her/protege-ws- cfp.html for more details