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Rules are often used in conjunction with other declarative knowledge representation formalisms, such as ontology languages (e.g. RDF and OWL), in order to provide greater expressive power than is provided by either formalism alone. Ontology languages, for example, typically provide a richer language for describing classes (unary predicates). Rules, on the other hand, typically provide a richer language for describing dependencies between properties (binary predicates), and may also support higher-arity predicates.

Rich domain models combining both rules and ontologies are often needed in domains such as medicine, biology, e-Science and Web services. In such domains, several actors and/or agents are involved that have to interchange the data, ontologies, and rules that they work with. An example is the use of such a domain model in an application that aims at assisting the labelling of brain cortex structures in MRI images. In this case, an OWL ontology is used to capture knowledge about the most important brain cortex anatomical structures, and a rule base is used to capture knowledge about mereological and spatial dependencies between properties.

For example, a rule is used to express the dependency between the ontology properties isMAEConnectedTo and isMAEBoundedBy, in particular (a simplified form of) the knowledge that two Material Anatomical Entities having a shared boundary are connected:

If MAE X is bounded by Z and MAE Y is also bounded by Z then X is connected to Y.

Benefits of interchange via RIF include the ability to collaboratively develop and share valuable knowledge, the ability to integrate anatomical images, possibly from distributed image sources, and the ability to use large-scale federated systems for statistical analysis of brain images of major brain pathologies.