  The delineation of logical definitions for each class in an ontology and the
consistent application of these definitions to the assignment of instances to
classes are important criteria for ontology evaluation. If ontologies are
specified with property-based restrictions on class membership, then such
consistency can be checked automatically. If no such logical restrictions are
applied, as is the case with many biological ontologies, there are currently no
automated methods for measuring the semantic consistency of instance assignment
on an ontology-wide scale, nor for inferring the patterns of properties that
might define a particular class. We constructed a program that takes as its
input an OWL/RDF knowledge base containing an ontology, instances associated
with each of the classes in the ontology, and properties of those instances.
For each class, it outputs: 1) a rule for determining class membership based on
the properties of the instances and 2) a quantitative score for the class that
reflects the ability of the identified rule to correctly predict class
membership for the instances in the knowledge base. We evaluated this program
using both artificial knowledge bases of known quality and real, widely used
ontologies. The results indicate that the suggested method can be used to
conduct objective, automatic, data-driven evaluations of biological ontologies
without formal class definitions in regards to the property-based consistency
of instance-assignment. This inductive method complements existing, purely
deductive approaches to automatic consistency checking, offering not just the
potential to help in the ontology engineering process but also in the knowledge
discovery process.
