Internet Reasoning Service: Progress Report

1. Internet Reasoning Service:Progress Report Wenjin Lu and Enrico Motta Knowledge Media Institute Monica Crubézy Stanford Medical Informatics

2. IRS: What it is? • Web-based tool to support reuse of reasoning services • Different levels of support • Manual browsing/configuration • Intelligent Assistant • In the long term: broker-mediated service

9. Generic Classification Task • Input roles • Candidate Solutions, Match Criterion, Solution Criterion, Observables • Precondition • Both observables and candidate solutions have to be provided • Goal • To find a solution from the candidate solutions which is admissible with respect to the given observables, solution criterion and match criterion

10. 3. Internet Reasoning Service

11. Task Selection • IRS provides • a graphical and browsable description of each generic task • examples of pre-existing instantiated task models. • Can we do more?

12. Task Configuration (application inputs) • Application inputs = case-independent ones • Instantiate by • Mapping to domain model • Solution Space -> Hierarchy of apple types • Directly filling task roles • Defining a new match criterion encoding constraint according to the relevant task ontology • Selecting from available options • choosing existing match criterion

13. Task Configuration (Case inputs) • No need to fill case inputs at this stage • Still, mappings may be required • Observables features -> apple properties

14. Task Model Verification • Task Model Verification = Checking task assumptions (only if they do not rely on case-specific inputs). • Can task assumptions rely on case-specific inputs?

15. PSM Selection • Through a direct link between a PSM and a task. • e.g., in OCML PSMs are linked to the tasks that they can solve by a special slot “tackles-task”. • Through an existing PSM-Task bridge • As the result of users’ choice among available PSMs. • IRS will need to support the creation of relevant PSM-Task Bridge • As the result of a competence matching process between the task and available PSMs. • Competence matching should generate appropriate PSM-Task bridge

16. PSM Configuration • Same as task configuration • Roles inherited from relevant task • PSM may define additional roles • e.g., heuristic classifier introduces abstractors and refiners

17. PSM Verification • Checking PSM Assumptions • again, only if no case-specific roles are involved

18. PSM Execution • Acquiring case-specific input from user. • Checking precondition/assumptions • Calling the PSM code with the mapped inputs. • Interpreter may be local or remote • Displaying the progress of the PSM execution, at least in a console window (that assumes that the code interpreter or the PSM code sends trace messages to the console). • Filling-in the domain outputs with the results of PSM execution (through mapping relations) and presenting those results to users.

19. Possible Platforms for IRS • Specialized WebOnto Configuration • Unlikely • Nobody working on it • Protégé • Based on pre-existing PSM Librarian plug-in • Monica working on it • New Java/Lisp Tool • Java Applets interfaced with library sitting on Lisp server • Wenjin working on it.

20. IRS in Protege

21. Additional Developments • Classification library to be tried out in 2 domains • E-commerce • user classification, product selection • configuration of ‘user basket’ • will use parametric design library • Paleontology • Classification is everything in Paleontology • Complicated problem • No agreed hierarchy/classification rules • gaps in the models