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KMi-SMI collaboration
Wenjin Lu and Enrico MottaKnowledge Media Institute
Monica CrubézyStanford Medical Informatics
Goals
• Customizing Protégé editor for use with OCML (Protégé-OCML)– Evaluating Protégé suitability
• Translating classification library into Protégé-OCML– Validating classification library– Evaluating UPML
• Revising Specification for Internet Reasoning Service
1. The Protégé-UPML editor for OCML
• Modeling of OCML meta ontology in Protégé– i.e., classes, relations, functions, axioms, etc...
• Inclusion of OCML meta ontology in Protégé-UPML editor– i.e., defining the appropriate KA forms for the basic OCML
constructs
• Extension of UPML concepts for OCML, with reference to OCML meta ontology– e.g., formula -> OCML-formula
OCML meta ontology in ProtégéBy the way, this is the output of a new plugin for Protégé,
that creates a graph out of a KB,with different visualization options
subclass-of linkinstance-of link
Each OCML construct is an instance of :OCML-CLASS
Template slots define own OCML
fields for all classes
OCML in Protégé-UPML editor
• Inclusion of OCML meta ontology in UPML domain modelling meta-ontology
• Extension of UPML concepts for OCML (“UPML-OCML concepts”)
– Formula < OCML-Formula • formula -> ocml-kappa-expression, ocml-relation-mapping
– Program < OCML-Program• program -> ocml-lambda-expression, :OCML-PROCEDURE
– Signature Element < OCML-Element • ocml-type -> :OCML-CLASS, :OCML-RELATION, :OCML-FUNCTION
– Signature < OCML-Signature • signature-elements -> (OCML-Element)
UPML core ontology isextended for OCML
2. The Classification library in UPML
Task-DomainBridge
PSM-DomainBridge
PSM-TaskBridge
Task
Task Refiner
DomainModel
Domain Refiner
PSM
PSM Refiner
Ontologies
Ontology Refiner
Classification Library
Heuristicclassifier
(Optimal)Classification
Optimal classification@ Heuristic classifier
Apples
Heuristic classifier@ Apples
Optimal classification@ Apples
Abtractor,Refiner
Color “green”,Sugar-level
Observable, Solution
Abstractor = Sugar-abstractorSolution = Apple class
The Classification library in Protégé-UPML
• Classification ontologies modeled in Protégé-OCML– Classification task, heuristic classification method, apples
domain, apples @ heuristic classification application– Modeled as separate ontologies in Protégé-OCML (ie, a
hierarchy of classes that are instances of OCML primitives)– Included along with UPML-OCML (UPML extended for OCML)
• UPML-OCML concepts instantiated with elements of the included classification ontologies
• UPML concepts instantiated with classification tasks, PSMs, knowledge roles, etc.
Classification ontologies in Protégé-OCML
OCML-BASE
CLASSIFICATION-TASK
HEURISTIC-CLASSIFICATION-METHOD
APPLE-DOMAIN
APPLE-HEURISTIC-CLASSIFICATION-APPLICATION
included-inincluded-in
included-inincluded-in
included-in
Each concept is an instance of
:OCML-CLASS
Own slots contain OCMLdefinition of the class
Classification Library instance
Classification Task instance
Task: Classification (ontology signature)
Task: Classification (competence)
Heuristic optimal solution classifier PSM
Heuristic optimal solution classifier PSM (Signature and Competence)
Heuristic optimal solution classifier PSM (Operational description)
Classification-task @ apples-domain bridge
4 mapping axioms define the bridge
Classification-task @ apples-domain bridge (mapping axiom for observable)
Conclusions of the experiment
• Customization of Protégé-UPML for OCML– 2-step process: OCML in Protégé + extension of UPML for
OCML– Reasonably easy to do!
• Classification library in UPML– Fits nicely, except:
• Knowledge roles (case-indep. data) at the task & complex PSM level
• Task, PSMs, etc. are classes in OCML, not instances as in UPML
• Needs the implementation of a connection to OCML interpreter
3. Internet Reasoning Service