What Is a Data Ontology?

What Is an Ontology?

An ontology is a model for organizing structured and unstructured information using entities, properties and the relationships between them. Like taxonomies, they help organizations classify information.

Ontology Example
Example Ontology demonstrating relationships to and from the entity "Employee"

Ontologies vs. Taxonomies

The key difference between an ontology and a taxonomy is complexity. 

While both ontologies and taxonomies provide a means of classifying information, ontologies provide richer context and information.

Within taxonomies, information is classified in one-dimension. Many online retail stores group their information taxonomically. 

An example of a taxonomy in a department store could be:

  • Furniture is classified into beds, couches, drawers, etc.
  • beds can be further classified into sizes - single, twin, queen-size and king-size 
  • and then further classified into brands
  • the taxonomy would eventually end in a single entity or group of entities that match all of the cub-criteria

In many cases, this degree of specificity is sufficient. However, the limitations of taxonomies mean they can only define categories within one domain. Relationships across domains are left unrepresented. 

This is where ontologies take things further. An ontology allows for multi-dimensional - or “inter-taxonomical” - mapping. The ability to map between taxonomic structures can help finding and recommending relevant information. In fact, ontologies are key in enabling product recommendations.

For example, with an ontology, a query for “beds” would identify a relationship between beds and “hand tools” used for assembling furniture - despite hand tools and beds belonging to very different taxonomies. 

How Ontologies Relate to Data and Data Democratization

Ontologies greatly aid in the categorization and discovery of data. 

In fact, the ability to search the incredible amount of data on the web efficiently, is partly due to the ontologies that describe their relationships. 

So as the value of data increases, data ontologies are becoming increasingly valuable, also. 

The multi-dimensional mapping of entities and data assets is key to organizations efforts to democratize data and manage complex data ecosystems. 

In light of increasingly data-driven organizations and consumers, a dedicated language for constructing ontologies, the Ontology Web Language (OWL), has been developed by the W3C

This highlights the importance of ontologies in the growth of the semantic web by providing tools to streamline their creation. 

How Does a Business Use an Ontology?

Data-driven organizations are challenged by the complexity and volume of information that needs to be handled efficiently to meet business objectives. Following are some of the specific ways that ontologies can help companies address this challenge.

  • An ontology can be used to understand how a term’s usage has changed over time so it can be placed in context and used correctly.
  • Ontologies can synchronize data in multiple repositories to define a common language across an industry or business sector.
  • An ontology allows people and applications to share a common understanding of the structure of the information in a domain. 
  • Machine learning, including the ability to independently make deep, non-intuitive decisions, is facilitated by the use of ontologies.

Having a more complete understanding of data assets and their relationships assists businesses to make the most effective use of them.  

Developing an Ontology

Ontologies are complex constructs that require data as raw material. This can be in the form of a business glossary, which is a collection of business terms and definitions. So a logical first step in developing an ontology is to build a business glossary. Once a business glossary is constructed, it can be used as the foundation of an ontology. 

An illustrative example of how a business glossary is used when developing an ontology is this outline proposed by the School of Information and Computer Sciences at the University of California, Irvine. In this model, an ontology consists of these components:

  • A glossary of concepts;
  • Sub-concept and instance-part hierarchies of the concepts;
  • Properties associated with each concept defining instance-property relationships;
  • Additional relationships that can be identified between concepts;
  • Cardinal constraints on relationships and functions between concepts.

The glossary is an indispensable part of an ontology. The resulting ontology defines complex relationships between elements that are beyond the scope of a glossary. The ontology itself can now be used as the basis for data modeling for implementation in a relational database.

Tools for Data Modeling and Using Ontologies

Data modeling tools help organizations make more effective use of data ontologies. Data models can be used to create the data glossaries required as raw material for ontologies

With the right data modeling solution, organizations can harvest business terms and the relationships between them from existing logical data models. This greatly reduces the amount of manual work required to build an ontology and business glossary.

In the absence of an Enterprise Data Model, this can work in the reverse. I.e. an accepted ontology can be transformed into new logical and then physical data models that can be implanted in databases. By bringing together disparate data models of data assets, this approach greatly aids in the data governance and data cataloguing journey. 

With ER/Studio, organizations can take advantage of this functionality and augment their data governance and data modeling initiatives.

The ER/Studio family of data modeling applications helps discover data assets and offers the functionality necessary to create effective data glossaries for use in developing ontologies.

ER/Studio Business Architect

Business Architect maps the relationships between the people, processes, and data resources that form the foundation of a business glossary. Conceptual data models can be created to define business objectives.

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ER/Studio Data Architect

ER/Studio Data Architect is used to create logical and physical data models. Consistency between data models can be enforced through naming standards, facilitating their use throughout an organization.

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ER/Studio Enterprise Edition

ER/Studio Enterprise Edition is a collaborative tool that coordinates updates and maintains consistency across data models through the use of a shared repository. 

Enterprise Edition can be used to build an enterprise-wide business glossary that provides a common data vocabulary and can be essential when developing a data ontology.

Request a free demo of ER/Studio Enterprise Edition!

Creating a business glossary and data ontology requires teams to use the proper tools. ER/Studio furnishes the collaborative functionality teams need to effectively use their data resources. 

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