What is an ontology?

An ontology is a formal specification that provides sharable and reusable knowledge representation. Examples of ontologies include:

  • Taxonomies
  • Vocabularies
  • Thesauri
  • Topic Maps
  • Logical Models

An ontology specification includes descriptions of concepts and properties in a domain, relationships between concepts, constraints on how the relationships can be used and individuals as members of concepts.

In the example below, we can classify the two individuals, Fred and Wilma, in a class of type Person, and we also know that a Person is a Mammal. Fred works for the Slate Rock Company and the Slate Rock Company is of type Company, so we also know that Person worksFor Company.


What are the benefits of developing and using an ontology?

First, ontologies are very useful in gaining a common understanding of information and making assumptions explicit in ways that can be used to support a number of activities.

These provisions, a common understanding of information and explicit domain assumptions, are valuable because ontologies support data integration for analytics, apply domain knowledge to data, support application interoperability, enable model driven applications, reduce time and cost of application development, and improve data quality by improving meta data and provenance.

The Web Ontology Language, or OWL, adds more powerful ontology modeling means to RDF and RDFS. Thus, when used with OWL reasoners, like in GraphDB, it provides consistency checks, such as are there any logical inconsistencies? It also provides satisfiability checks, such as are there classes that cannot have instances? And OWL provides classification such as the type of an instance.

OWL also adds identity equivalence and identity difference, such as, sameAs, differentFrom, equivalentClass, and equivalentProperty.

In addition, OWL offers more expressive class definitions, such as, class intersection, union, complement, disjointness and cardinality restrictions.

OWL also offers more expressive property definitions, such as, object and datatype properties, transitive, functional, symmetric, inverse properties, and value restrictions.

Finally, ontologies are important because semantic repositories use them as semantic schemata. This makes automated reasoning about the data possible (and easy to implement) since the most essential relationships between the concepts are built into the ontology.

Using ontologies in GraphDB

To load your ontology in GraphDB, simply use the import function in the GraphDB Workbench. The example below shows loading an ontology through the GraphDB Workbench Import view:


See also

You can also watch the video from GraphDB Fundamentals Module 3: