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.
Example Ontology demonstrating relationships to and from the entity "Employee"
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:
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.
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.
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.
Having a more complete understanding of data assets and their relationships assists businesses to make the most effective use of them.
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:
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.
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.
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.
Try ER/Studio Business Architect for Free!
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.
Try ER/Studio Data Architect for Free!
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.
Try ER/Studio for free!
Powered by IDERA