Choosing the appropriate ontology management system for your research

Collaboration is a key feature of an OMS, as ontologies are built with the help of different stakeholders. Inputs are required from ontologists who manage syntax and standards; domain experts who ensure the ontology covers the target domain sufficiently and correctly apply relationships, rules, and terminology; and analysts who ensure  the ontology covers the use case.

The ability to import ontology from filesystem and public repositories, and to export ontologies in multiple serializations.

Each entity (class, predicate, or instance) in ontologies requires a unique URI. URI is, therefore, a key part of ontology management, and retaining URI uniqueness is an essential functionality in an OMS.

Stakeholders can use an OMS to search for terms in ontologies, so efficient OMS makes it possible for users to look up specific terms. Terms are found in multiple ontologies, and identified terms can be active or deprecated, so filtering and sorting search teams is a critical requirement of an effective OMS.

A key objective of an OMS is to allow the users to efficiently create and edit ontologies in a user-friendly environment.

An audit trail helps track recent changes made to ontologies. It also adds transparency and traceability to the ontology management process by highlighting changes and identifying the user that made them.  Audit trail logs support business decision-making, as they reveal how much effort is being spent on the management of ontologies, which users are participating in the process, whether there are blockers, etc.

One way to control access to ontologies in a management system is by assigning permission tiers to different user groups.  Such a feature would be helpful when there are stakeholders who should only be reading – but not editing – ontologies,  or editing specific ontologies but not all.  Sometimes it’s also possible to restrict the level of write permissions for users. There could be specific domain experts who can suggest changes in the ontologies, but those changes may require peer review and approval before they are processed.

Ontologies are generally built using standard Semantic Web technologies such as RDF,  OWL, or SKOS. Standard query languages like SPARQL can also be used to assess the quality of ontologies. For interoperability and importing or reusing existing Semantic Web-based ontologies, it’s advisable to ensure the OMS supports the W3C recommended standards for building ontologies and vocabularies.

Versioning is an important feature of an efficient ontology management process. Exposing the ontologies to downstream applications is often required, which requires sufficient version control.

The ability to integrate external systems and applications is another desirable feature of an OMS. Many applications are available to use ontologies directly for many purposes, such as dropdown creation, data validation, data integration, natural language processing (NLP), and named-entity recognition (NER).

As ontologies are developed and reused, quality control becomes a very desirable functionality.  Potential quality constraints include incorrect relationship queries, correction annotations, missing domain and range, class coverage, etc. The ability to produce a quality report for each ontology is also an advantage.

Ontologies are built iteratively, often requiring multiple change rounds before reaching a mature state. Each iteration may include inputs from stakeholders such as domain experts, product managers, data analysts, or ontologists. An effective change management process captures and tracks change requests from stakeholders. It also allows efficient tracking of physical changes observed in the ontology following the change request.

RDF-based ontologies can be queried using SPARQL. The advantages of query functionality include the ability to perform rapid quality and consistency checks. Users can also extract data from ontologies to expose to downstream applications.

Ontology visualization represents ontologies in interactive graphics.

Machine aid speeds up the process of ontology building and management. This aid can help users add annotations to a concept and map similar concepts to one another.

Ontologies can be reused entirely, or specific concepts can be extracted to meet the requirements. However, it’s important to assess if an OMS allows importing of entire ontologies into one another, and whether the tool allows the borrowing of specific concepts from a reference ontology to be reused in a new ontology.