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Whether your lab business needs to catalog thousands of test methods or trace how a sample moves through a lab workflow, the way you organize information directly affects your lab’s efficiency, compliance, and ability to scale. The solution is to use clear, consistent, and meaningful information structures.
Taxonomies and ontologies are two building blocks labs can use for organizing and modeling lab data, but these two methods are best used for different purposes. Understanding the distinctions between them can help your lab organize and leverage the wealth of data you’re generating daily. They can also help you design smarter and more functional informatics systems, providing an even stronger foundation for business growth.
A taxonomy is a hierarchical classification system used to organize data or concepts into parent-child relationships. Typically arranged in a tree structure, a taxonomy contains broad categories that branch into more specific ones.
In a lab environment, taxonomies can help categorize data, including tests, instruments, sample types, and documents. For example, if your lab is organizing its test catalog, you could use a taxonomy to structure types of tests within broad categories:
Or for instrumentation:
Similarly, taxonomies can help labs enhance their informatics systems. They can organize information in structured menus and drive navigation within lab software, such as a laboratory information management system (LIMS) or electronic lab notebook (ELN), and enable the filtering or grouping of data in reports and dashboards.
One of the strengths of taxonomies is that they are simple to build and manage by internal IT teams or external consultants because they follow a single-inheritance model. Furthermore, they are generally easy for lab staff to understand and maintain, and useful for reporting and user interface (UI) consistency.
However, they do suffer some limitations. For example, concepts can only belong to a single category at a time. Taxonomies also fail to capture relationships between categories, such as which test measures what substance, and they lack the depth to support automation or complex reasoning when applied to a lab informatics system.
An ontology, on the other hand, goes far beyond classification. It’s a model that defines concepts, their properties, and the relationships between them, whether those are associative, hierarchical, or causal. Ontologies are built to be understood by both humans and machines.
While taxonomies answer the question “Where does this belong?”, ontologies can answer the questions “What is this?”, “How does it relate to other things?”, and “What can I infer from that?”
For example, for a glucose test, an ontology might describe:
This structure allows informatics systems to reason over the data. For instance, if lab staff search for “tests that measure sugars,” the system includes the glucose test because it knows glucose is a type of sugar, based on information derived from the ontology.
A strength of ontologies is their ability to describe complex lab concepts and relationships. They can also support automation, enable advanced analytics and machine reasoning, and provide a foundation for interoperability and the sharing of information with other organizations.
However, because they can be more complicated to develop and govern, they might not be the right approach for all lab applications, especially those with simple or well-contained data needs. Furthermore, they can require expertise in specialized languages and tools such as:
While both methods can help labs map entities and workflows, most labs start with taxonomies. They’re easy to implement and handle many of a lab’s day-to-day needs. However, as labs mature, especially in regulated, research, or networked environments, they need to map more complex relationships between data. That’s when ontologies become valuable. They support consistent terminology across teams, better regulatory traceability, and more intelligent data integration and automation.
For example, an ontology would be a better choice if:
Nevertheless, taxonomies are a good choice for simpler organization tasks, such as building a folder structure or developing drop-down menus in lab software.
Data is an increasingly important asset for lab businesses. Structuring data using a taxonomy can give you the order you need to create more efficient workflows. Ontologies, on the other hand, offer a more nuanced view of the data and their relationships. That makes them the best choice if you need to understand complex relationships or infer new knowledge.
Better yet, the appropriate use of taxonomies and ontologies can help your lab develop a well-organized and intelligent lab informatics system — providing your business with numerous advantages, from the ability to scale throughput rapidly to the potential to create exciting innovations to advance science or even save lives.
Do you need help implementing a taxonomy or ontology in your informatics systems? Contact us today.
