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Consistency Metrics

  • CS1: detection of use of entities as members of disjoint classes using the formula: no. of entities described as members of disjoint classes/total no. of entities described in the dataset [19,31,37]
  • CS2: detection of misplaced classes or properties* using entailment rules that indicate the position of a term in a triple [18,31]
  • CS3: detection of misuse of owl:DatatypeProperty or owl:ObjectProperty through the ontology maintainer** [31]
  • CS4: detection of use of members of owl:DeprecatedClass or owl:DeprecatedProperty through the ontology maintainer or by specifying manual mappings from deprecated terms to compatible terms [18,31]
  • CS5: detection of bogus owl:InverseFunctionalProperty values (i) by checking the uniqueness and validity of the inverse-functional values [31], (ii) by defining a SPARQL query as a constraint [37]
  • CS6: detection of the re-definition by third parties of external classes/properties (ontology hijacking) such that reasoning over data using those external terms is not affected [31]
  • CS7: detection of negative dependencies/correlation among properties using association rules [11]
  • CS8: detection of inconsistencies in spatial data through semantic and geometric constraints [50]
  • CS9: the attribution of a resource’s property (with a certain value) is only valid if the resource (domain), value (range) or literal value (rdfs ranged) is of a certain type - detected by use of SPARQL queries as a constraint [37]
  • CS10: detection of inconsistent values by the generation of a particular set of schema axioms for all properties in a dataset and the manual verification of these axioms [64]

* For example, a URI defined as a class is used as a property or vice-a-versa.

** For example, attribute properties used between two resources and relation properties used with literal values.

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