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Taxonomies and ontologies are important methods for organizing and describing lab data. But what if you want to share your lab’s data externally? You might need to communicate a patient’s test results with a hospital’s electronic health record (EHR) system, submit data to a regulatory authority, or publish findings linked to public research databases. To accomplish these tasks efficiently, you need Semantic Web standards and technologies.
The Semantic Web is a framework and associated technologies designed to make data not only machine-readable but also machine-understandable. While taxonomies are valuable for hierarchical classification, and ontologies can model complex relationships between data, the Semantic Web takes structured data a step further, supporting interoperability between systems and outside the lab.
The Semantic Web — also known as Web 3.0 — is an extension of the World Wide Web that allows data to be linked and interpreted based on its meaning or semantics rather than just its format. Because it applies formal standards to data, it enables computers to automatically connect, integrate, and reason over commonly structured data from multiple sources.
The Semantic Web relies on several key technologies, all maintained by the World Wide Web Consortium (W3C):
Together, these elements enable the sharing of data and the data’s meaning across computer systems — this is called semantic interoperability.
If your lab wants to share data beyond its internal informatics system, implementing semantic web technologies is a key step for five reasons:
By using shared vocabularies such as LOINC, SNOMED CT, or the Ontology for Biomedical Investigations (OBI), your lab can ensure its data is commonly understood everywhere. That means, for example, that the “Glucose Test” in your laboratory information management system (LIMS) can be recognized as the same concept as “Glucose measurement” in a hospital’s EHR or a public database like LOINC.
Because of that shared meaning, your data can be integrated with:
Semantic technologies reduce errors introduced by mismatched terminology. For example, “HbA1c” and “Glycated Hemoglobin” can be automatically recognized as the same analyte, preventing misinterpretation in downstream analysis.
When data is semantically structured, machine learning models and AI tools can:
Automation is critical for labs planning to scale throughput or increase capacity, especially during unexpected scenarios such as the COVID-19 pandemic, when labs were under pressure to scale testing rapidly. It’s also important to consider if your lab is seeking to be agile and responsive to a changing market or future business strategies.
Regulators are increasingly expecting data submissions in standardized formats. Using Semantic Web principles makes it easier to:
Semantic Web technologies also ensure your lab’s data adheres to FAIR principles. This means the data is findable, accessible, interoperable, and reusable, which is useful for supporting intercompany activities.
Pistoia Alliance published a report in May 2025, “Beyond Research: Realizing the Value of FAIR Initiatives, that describes the key outcomes delivered for companies implementing FAIR data principles. They reveal that “FAIR initiatives that started five or more years ago now deliver tangible improvements in data management processes and enhanced operational efficiency.”
The GO FAIR Initiative coordinates implementation networks and a three-pillar strategy that organizations can use to address socio-cultural change, skills development training, and technical standards, best practices, and infrastructure components. If your business is interested in implementing FAIR principles, you can join an existing implementation network or form a new one.
If your lab wants to link data to public ontologies, you could use the Semantic Web in the following way:
While the Semantic Web is enormously powerful, implementing these technologies in a lab informatics system can present several challenges.
The first is data modeling expertise. Using these technologies requires knowledge of ontologies and linked data standards. If your internal informatics team doesn’t already have this expertise, you might need to work with an external consultant familiar with using the Semantic Web in a lab environment.
Your lab will also need to ensure you have governance in place for your structured data. This means having systems for maintaining mappings and standards across evolving datasets.
Another consideration will be the infrastructure of your lab informatics system. Your lab will need solutions that support RDF storage and SPARQL querying. Some LIMS, such as Labbit, come with these features already built in. However, in many cases, you’ll need to work with a consultant to implement Semantic Web standards within your existing systems.
Lastly, your lab will also need to ensure your staff understand how semantic data fits into their workflows, and the importance of maintaining structured data for consistency and interoperability. This may require additional training and a robust change management program so everyone is up to speed on your lab’s evolving informatics strategy.
As scientific collaboration becomes more global, the Semantic Web can transform lab informatics systems from isolated data silos into connected, interoperable knowledge systems.
Labs can start small by adopting existing biomedical ontologies such as LOINC, SNOMED CT, or OBI. If they have the expertise, they can use tools like Protégé to model domain concepts and export key datasets in RDF format, or they can partner with external consultants experienced in both Semantic Web technologies and the complexities of the laboratory business and regulatory requirements.
By incorporating the Semantic Web within your informatics strategy, your lab can establish a foundation for scalable, cross-domain data exchange and long-term business and scientific value.
If you want to implement taxonomies, ontologies, and semantic web standards as part of a scalable, future-proof lab data strategy, contact us today.
