Ontology Application Framework

[Michael Gruninger <mudcat@xxxxxxxxxxxxxxx>]

Attached is a revision of the Ontology Application Framework that
was originally presented at the Feb 3 telecon.    (01)

- michael    (02)

Ontology Application Framework

The objective of the framework is to provide a common terminology
for describing applications of ontologies and the benefits that
ontologies deliver within these applications.
In addition to classifying ontology applications,
this provides the basis for benchmarks and the ability to compare
different applications of ontologies.

Dimensions

1. Functionality
   How are the ontologies used?
   • Matching / mapping of concepts
   • Automated inference
   • Classification
   • Specification
2. Architecture
   Are the ontologies being applied to a single system or are they being used among multiple systems?
3. When are the ontologies used?
   Are the ontologies used at design time for the system or are they used by the system at runtime?
4. Who is using the ontologies?
   
   • Ontology Author
   • Data Author
   • Application Developer
   • Application User
   • Knowledge Worker
5. What ontologies are used?
   
6. Problem addressed
   Why are the ontologies being used?
7. Benefit
   What is the impact of the ontologies on the functional/nonfunctional requirements of existing system?
   • The ontologies support new functional requirements within system.
   • The ontologies support nonfunctional requirements of the system: quality, performance, maintenance, cost, portability, reliability, scalability, robustness, usability, and extensibility.

     The value metrics for evaluating the benefits associated with nonfunctional requirements are the same metrics used for nonfunctional requirements themselves.

An Initial Classification of Ontology Applications

We can use the above ``dimensions" to identify the following classes
of applications.

1. Semantic integration
   • information integration
     Multiple information resources are combined using ontologies at runtime to match concepts with similar meaning.
   • database integration
     Queries that require multiple databases are specified using common ontologies and data schema are matched using these ontologies at runtime.
   • software interoperability
     Software systems exchange sentences that are written using ontologies. Each software software uses an ontology (either its own or a set of shared ontology) to translate the exchanged sentences at runtime.
   • software access
     Ontologies provide a uniform conceptual interface to multiple software applications.
2. Decision Support
   • deduction from Axioms via Theorem Provers
     The axioms of the ontology and domain theories are used to entail queries using a general-purpose automated theorem prover at runtime.
   • deduction from Axioms via Special-Purpose Reasoners
     Special-purpose algorithms are implemented that are sound (and possibly complete) with respect to the set of sentences that are entailed/satisfied by the ontology at runtime.
   • validation of integrity constraints
3. Semantic Augmentation
   • ontology-based algorithms
     Existing algorithms use classes and relations within ontologies at design time to define search space and specify heuristics to guide search
   • ontology embedded into software
     Object-oriented implementation (e.g. Java classes) generated from classes in the ontology at design time.
   • model-driven design
     Design (enterprise or manufacturing artefact) is specified using the ontology and exported into software.
   • ontology-based standards
     Terminology within a standard is axiomatized by an ontology. Conformance with the standard is evaluated with respect to this axiomatization.
4. Knowledge Management
   • discovery
     Patterns in data are identified by matching against the classes, relations, and axioms of the ontology
   • categorization of information resources
     An ontology is used to organize unstructured information resources by identifying classes and relations within the ontology with terms that appear in the resources.
   • knowledge capture
     Classes in the ontology provide templates for gathering knowledge from subject matter experts.

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