Knowledge Path Series: 7. Ontologies

Navigate your Data with a Map

Did you know that GraphDB™ provides support for Ontologies at various levels of sophistication?

Importance of Ontologies

Ontologies are important because semantic repositories use ontologies 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.

In general, an ontology formally describes a (usually finite) domain of related concepts (classes of objects) and their relationships. For example, in a company setting, staff members, managers, company products, offices, and departments might be some important concepts.

The relationships typically include hierarchies of classes. A hierarchy specifies a class C to be a subclass of another class C’ if every object in C is also included in C’. For example, all managers are staff members, all dolphins are mammals, all Senators are members of congress, etc..

Apart from subclass relationships, ontologies may include information such as:

  • properties (X is subordinated Y);
  • value restrictions (only managers may head departments);
  • disjointness statements (managers and general employees are disjoint);
  • specifications of logical relationships between objects (every department must have at least three staff members).

Formal knowledge representation (KR) is about building models. The typical modelling paradigm is mathematical logic, but there are also other approaches, rooted in the information and library science. KR is a very broad term; here we only refer to its mainstream meaning of the world (of a particular state of affairs, situation, domain or problem), which allow for automated reasoning and interpretation. Such models consist of ontologies defined in a formal language.

Ontologies can be used to provide formal semantics (i.e. machine-interpretable meaning) to any sort of information: databases, catalogues, documents, Web pages, etc. Ontologies can be used as semantic frameworks: the association of information with ontologies makes such information much more amenable to machine processing and interpretation.

This is because ontologies are described using logical formalisms, such as OWL, which allow automatic inferencing over these ontologies and datasets that use them, i.e. as a vocabulary. An important role of ontologies is to serve as schemata or “intelligent” views over information resources.

This is also the role of ontologies in the Semantic Web. Thus, they can be used for indexing, querying, and reference purposes over non-ontological datasets and systems, such as databases, document and catalogue management systems.

There are a wide range of Public Ontologies available ranging from highly-complex within complicated domains to very simple within straightforward domains. A sampling of publicly available ontologies are as follows:

GraphDB™ can work with your own custom ontologies in different flavors of RDF including XML, N3, N-Triples, N-Quads, Turtle, TriG & TriX.

Setup

To load your Ontology in GraphDB™, simply utilize the import function in Sesame RDF Workbench or our own GraphDB™ Workbench application. The example below shows loading an Ontology through the GraphDB™ Workbench.

Ontologies1

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