Main menu


Ten Guidelines for Adopting Ontologies to Create More Fair Scientific Data

In a previous article, we discussed how ontologies help address some of the big data challenges in life sciences by FAIRifying data to make it searchable, accessible, interoperable, and reusable. explained about Ontologies – human-generated, machine-readable models of the domain – help make data fair and usable from the moment of its creation. This reduces the time scientists spend searching for information, avoids duplicated experimental work, and makes the data “machine-ready” to power AI and machine learning projects.

However, deciding to implement an ontology in your data management practice can be difficult. It can also be difficult to market to business stakeholders as the ROI is often not immediate. This article provides an overview of the challenges and 10 guidelines to start your ontology journey.

Business, cultural and scientific challenges

Implementing an ontology is a special task. To do this successfully, data collected from many sources must be consistently formatted, structured, and harmonized. In a data-heavy field, this is a challenge. But in life sciences, the challenge is particularly acute. Data sources include published literature, experimental data, patient and clinical records, including graphs and tables, biomedical images, social media data, and voice recordings.

Life sciences organizations must also consider business and regulatory requirements. Companies want to ensure that every ontology follows strict governance processes and has robust version control to provide a visible audit trail. You also need a system that is agile enough to make changes easily. Building a network of ontologies that simultaneously allows for these levels of flexibility and control is difficult and time consuming.

Additionally, there is a growing demand for ontologies to be more ‘democratic’, allowing different users across the business to contribute to their development. This broadens the pool of knowledge that feeds onto the ontology, making it more accurate and reflecting the needs of its users.However, this requires a change in cultural mindset – It’s no longer ‘it’s my lab, it’s my data’. Rather, “it’s the company’s data and its FAIR.”

The final and potentially most important challenge is proving the value of the ontology and the FAIR project to stakeholders. As with any large, complex project, the ROI is medium- to long-term, and an ontology project can be jeopardized in the short term. Therefore, pay attention to the following 10 points to maximize the success of your ontology project.

1. reveal what already exists

Before planning a new project, the data team should identify ontologies already in use within the organization. Whether it’s a public ontology or a bespoke terminology created in-house. Building on existing work accelerates progress and provides early results to present to stakeholders.

  1. Rebuild, Reuse, Recycle

Work on life science ontologies has been going on for decades, which means there are existing open source frameworks available.public ontologies such as mesh A great starting point from NIH. Using what is already available as the basis for your own ontology is an easy way to make tangible progress.


The most successful companies I know are those with “FAIR Champions” who understand the above challenges. FAIR champions don’t have to be semantics or data science experts. You need to be tenacious, committed to the project, and able to motivate stakeholders around goals and milestones.

Four. Create a URI strategy

At the beginning of an ontology journey, a Uniform Resource Identifier (URI) must be established. URIs, like URLs in web addresses, provide a means of locating and retrieving resources on a network. A URI represents a unique ID for an entity and is difficult to change once set. Adopting a common URI strategy from the start reduces the chance of error and promotes standardization across your business.

  1. map sparingly

Mapping an ontology is a long, never-ending task, with ever-moving targets, as the ontology evolves with the understanding of the life sciences. Try to limit the mapping as much as possible by limiting yourself to a small number of ontologies (ideally one!) per domain and not bringing in or creating new ontologies that are already in use in the domain. please.

6. Simplify ontology selection

Minimizing the number of ontologies used reduces the burden of synchronizing them and mapping between them. Choosing a public ontology further simplifies the integration of public and private data. For example, if the domain is sick, Mond disease ontology to reduce your workload.

  1. start small and repeat

You can’t tackle all your data at once. It takes too long to verify the return. Besides, it’s probably not possible. Start with one use case Time – Build prototypes to see what works and use those learnings to iterate. Data entry projects such as assay registrations are good starting points because they already have a specific structure. This could be a simple exchange from free text input to selection from a dropdown list of assays from the domain ontology of choice. This makes the data fair from the start. Standard lists ensure that information is recorded consistently, is interoperable, and facilitates future reuse.

  1. Don’t let the scale of the problem fool you

Organizations do not need a model of their overall strategy before starting an ontology project. As mentioned earlier, repeat success is key. For example, consolidate lists of terms and upload them centrally where people can contribute, or start with areas where you already know relatively good data management you can build to show value quickly.

  1. find business value

One of the challenges in data management efforts is that the business value is medium- to long-term. Find short-term impacts and connect them to business outcomes to secure funding and move projects forward. For example, we show that applying the ontology to the creation of bioassays reduced the time spent searching for data by X hours. Or, show how using an ontology has enabled the reuse of valuable datasets that were previously siled.tangible results Must It should be shared early and often with business leaders.

  1. Empower Subject Matter Experts

Empowering and trusting subject matter experts is essential. This includes both data scientists and domain experts who can provide the relationship and domain knowledge to successfully develop an ontology. Give them the right tools to do their job and a realistic time frame to deliver.

Driving future innovation

The use of ontologies in data management is fundamental to driving future innovation. Innovative life sciences leaders invest time and resources into embedding robust data practices. They know that the more efficient use of the data generated by scientists, the faster the path to new discoveries. False starts, dead ends, or getting off track are more common than they should be. This can be demotivating and frustrating.

Shortening the drug discovery lifecycle is not only valuable in terms of shareholder value and patient benefits, it also increases team productivity. Scientists work harder when they are certain that the path they are pursuing will ultimately succeed or “fail quickly.” With the right strategy and expertise, organizations can use ontologies to stay at the forefront of new breakthroughs.