What are Ontologies?

An ontology is a formal description of knowledge as a set of concepts within a domain and the relationships that hold between them. It ensures a common understanding of information and makes explicit domain assumptions thus allowing organizations to make better sense of their data.

What is Ontology

An ontology is a formal description of knowledge as a set of concepts within a domain and the relationships that hold between them. To enable such a description, we need to formally specify components such as individuals (instances of objects), classes, attributes and relations as well as restrictions, rules and axioms. As a result, ontologies do not only introduce a sharable and reusable knowledge representation but can also add new knowledge about the domain.

The ontology data model can be applied to a set of individual facts to create a knowledge graph – a collection of entities, where the types and the relationships between them are expressed by nodes and edges between these nodes, By describing the structure of the knowledge in a domain, the ontology sets the stage for the knowledge graph to capture the data in it.

There are, of course, other methods that use formal specifications for knowledge representation such as vocabularies, taxonomies, thesauri, topic maps and logical models. However, unlike taxonomies or relational database schemas, for example, ontologies express relationships and enable users to link multiple concepts to other concepts in a variety of ways.

Do you want to learn how shared understanding of the data and its meanings helps your enterprise knowledge management?

New call-to-action

As one of the building blocks of Semantic Technology, ontologies are part of the W3C standards stack for the Semantic Web. They provide users with the necessary structure to link one piece of information to other pieces of information on the Web of Linked Data. Because they are used to specify common modeling representations of data from distributed and heterogeneous systems and databases, ontologies enable database interoperability, cross-database search and smooth knowledge management.

Ontologies for Better Data Management

Some of the major characteristics of ontologies are that they ensure a common understanding of information and that they make explicit domain assumptions. As a result, the interconnectedness and interoperability of the model make it invaluable for addressing the challenges of accessing and querying data in large organizations. Also, by improving metadata and provenance, and thus allowing organizations to make better sense of their data, ontologies enhance data quality.

The OWL Standard and Ontology Modelling

In recent years, there has been an uptake of expressing ontologies using ontology languages such as the Web Ontology Language (OWL). OWL is a semantic web computational logic-based language, designed to represent rich and complex knowledge about things and the relations between them. It also provides detailed, consistent and meaningful distinctions between classes, properties and relationships.

By specifying both object classes and relationship properties as well as their hierarchical order, OWL enriches ontology modeling in semantic graph databases, also known as RDF triplestores. OWL, used together with an OWL reasoner in such triplestores, enables consistency checks (to find any logical inconsistencies) and ensures satisfiability checks (to find whether there are classes that cannot have instances).

Also, OWL comes equipped with means for defining equivalence and difference between instances, classes and properties. These relationships help users match concepts even if various data sources describe these concepts somewhat differently. They also ensure the disambiguation between different instances that share the same names or descriptions.

The Benefits of Using Ontologies

The Benefits of Using Ontologies

One of the main features of ontologies is that, by having the essential relationships between concepts built into them, they enable automated reasoning about data. Such reasoning is easy to implement in semantic graph databases that use ontologies as their semantic schemata.

What’s more, ontologies function like a ‘brain’. They ‘work and reason’ with concepts and relationships in ways that are close to the way humans perceive interlinked concepts.

In addition to the reasoning feature, ontologies provide more coherent and easy navigation as users move from one concept to another in the ontology structure.

Another valuable feature is that ontologies are easy to extend as relationships and concept matching are easy to add to existing ontologies. As a result, this model evolves with the growth of data without impacting dependent processes and systems if something goes wrong or needs to be changed.

Ontologies also provide the means to represent any data formats, including unstructured, semi-structured or structured data, enabling smoother data integration, easier concept and text mining, and data-driven analytics.

Limitations of Ontologies

While ontologies provide a rich set of tools for modeling data, their usability comes with certain limitations.

One such limitation is the available property constructs. For example, while providing powerful class constructs, the most recent version of the Web Ontology Language – OWL2 has a somewhat limited set of property constructs. This concern has been addressed with RDF-Star, which allows one to make statements about other statements and this way to attach metadata to the edges in the graph

Another limitation comes from the way OWL employs constraints. They serve to specify how data should be structured and prevent adding data inconsistent with these constraints. This, however, is not always beneficial. Often, data imported from a new source into the RDF triplestore would be structurally inconsistent with the constraints set using OWL. Consequently, this new data would have to be modified before being integrated with what is already loaded in the triplestore.

A novel alternative to using ontologies to model data is using the Shapes Constraint Language (SHACL) for validating RDF graphs against a set of constraints. A shape specifies metadata about a type of resource – how it is used, how it should be used and how it must be used. As such, similarly to OWL, SHACL can be applied to incrementally validate data. Unlike OWL, however, SHACL can be applied to validate data that is already available in the triplestore.

Ontology Use Cases

Since ontologies define the terms used to describe and represent an area of knowledge, they are used in many applications to capture relationships and boost knowledge management.

The adoption of ontologies helps early hypotheses testing in Pharma by categorizing identified explicit relationships to a causality relation ontology. Ontologies also enrich semantic web mining, mining health records for insights, fraud detection and semantic publishing.

In a nutshell, ontologies are frameworks for representing shareable and reusable knowledge across a domain. Their ability to describe relationships and their high interconnectedness make them the bases for modeling high-quality, linked and coherent data.

Want to know about the role ontologies play to help interpret the data in a knowledge graph reliably and precisely?


Knowledge Graphs in the Enterprise_ The Story Behind the Hype White Paper Cover
White Paper: Knowledge Graphs in the Enterprise
The Story Behind the Hype

New call-to-action



Ontotext Newsletter