Everyone’s talking about biomedical ontologies! Let’s look at where most people go wrong and how to do it right.

The Trouble with Biomedical Ontologies

There’s a lot of confusion within the biomedical community as to what constitutes an ontology. It’s been aggravated by the impenetrable discourse used by ontologists similar to the way that mathematicians and computer scientists have a habit of using obscure mathematical notation to obfuscate their work. Even worse, this has led to confusion within the biomedical community as to what an ontology is for.

For example, biocurators find ontologies useful as a research tool for looking up the definitions of entities using tools like the Ontology Lookup Service (OLS). Database maintainers and biocurators often look to ontologies to decide what their data should look like and enable other people to understand it readily. Similarly, natural language processing (NLP) researchers find ontologies incredibly useful in named entity recognition because ontologies often contain preferred labels in many languages and synonyms for its terms. Software developers and end-users find ontologies useful for organizing and conveying information in a hierarchical manner.

While it might be true that ontologies have the ability to support all of those uses, none accurately portray ontologies. The following table presents more appropriate vocabulary to describe each of these use cases:

Phrase Definition
Semantic Space An enumerated set of entities
Controlled Vocabulary An enumerated set of entities and their names
Dictionary An enumerated set of entities and their definitions
Thesaurus An enumerated set of entities and their synonyms
Taxonomy / Hierarchy An enumerated set of entities with one parent for each
Multi-Hierarchy An enumerated set of entities with one or more parents for each

Database maintainers need controlled vocabularies to improve the utility of their databases, researchers can leverage dictionaries to learn the definitions, NLP developers need thesauri containing synonyms to train their models, and software developers need hierarchies or multi-hierarchies to support organization.

The actual purpose of an ontology is to first define the rules for how information is organized and second to apply those rules and actually store information. This means that ontologies can define that entities need an identifier, they need a preferred label, they can have synonyms, they can have equivalences to other entities in other ontologies, they can have relationships in the form of triples (like parent-child relationships), and they can even have relationships in the form of quadruples or higher-dimensional tuples. There are “upper-level” ontologies that take care of some of the common definitions that can be shared throughout a domain - you’ve probably noticed that most biomedical ontologies have this core of previously mentioned relationship types. That enables “lower-level” ontologies to focus on storing the information. Since most researchers only interact with the “lower-level” ontologies, it can be understood where the confusion came from.

In practice, this confusion is mostly harmless. We have numerous high-quality ontologies covering a huge amount of biology and related fields, as well as the OBO Foundry to vet them for quality and host them. Note: BioPortal is another place for hosting, but it seems like it has a much lower (or non-existent?) threshold for quality.

The danger lies in the edge cases. When there are no high quality biomedical ontologies in a given area, researchers are often inclined to generate their own. The rest of this blog post is about what happens next, where it all goes wrong, and how you can avoid it when you’re in the situation that you just became an ontologist, too.

How to Build Your Own Biomedical Ontology

The curation I trust most is by people who know what they’re doing, and more importantly by people who love what they’re doing.

Given the choice between curating information about a new protein (e.g. the proteins in the novel coronavirus) myself or having one of the excellent curators at UniProt do it, I would choose UniProt every time. Given the choice between the world’s leading coronavirus researcher and a UniProt curator with no previous knowledge about the novel coronavirus, I would still choose the UniProt curator. The UniProt curators love what they do, they know how to do it well, and they do it right. Same goes for lots of other groups that I’ve praised elsewhere in this blog. So keep in mind while you’re reading this guide that you might be causing more harm than good by making yet another ontology.


Before you start curating you need to do a bit of planning.

Pick a memorable name and prefix

There are a lot of ontologies, so pick a name that’s both unique and descriptive. Then, you need to pick a relatively short “prefix” which will be the first part of compact URIs (CURIEs) that point to entries in your ontology. Many ontologies use an acronym as their prefix, but make sure you don’t cause a conflict or confusion with a previously existing one. You can search through registries like Identifiers.org, the OLS, or the OBO Foundry (which, on a side note, don’t exactly contain all the same stuff) to check out the existing landscape.

Pick a scheme for identifiers

Even if you’re just here to build and maintain your controlled vocabulary, it’s still necessary to give identifiers to each of the entries in your ontology. In practice, each entry’s identifier should conform to the Minimal Information Requested in the Annotation of Biochemical Models (MIRIAM) standard. It states that identifiers should have the following five properties:

  1. Uniqueness
  2. Perenniality
  3. Standards-Compliant
  4. Resolvability
  5. Free Usability

There’s a really important thing to keep in mind - identifiers are not numbers. Even if they look like them, they’re strings. If that doesn’t make sense to you, then just think about what it would mean to “add” two identifiers to each other. It would be nonsense to think of identifiers as numbers because they don’t do what numbers do. With that in mind, there are a few common identifier schemes:

  • Identifiers that look like numbers, like PubMed identifiers. An example from PubMed is 29048466.
  • Identifiers that look like numbers, but are a fixed width with left-padded zeros. An example from the Experimental Factor Ontology is 0004859.
  • Identifiers that look like numbers, but are a fixed width with left-padded zeros and are prefixed with the prefix itself separated by a colon, so the identifier itself looks like a CURIE. An example from the Gene Ontology is GO:0006915. This is sort of confusing, and has been dubbed the GOGO problem. Or the Bananananana problem.
  • Identifiers that look like numbers, but are prefixed with part of all of the prefix. An example from ChEMBL is CHEMBL941 where the prefix is chembl.compound.
  • Identifiers that have a short letter prefix then a fixed width number with left-padded zeros. An example from MeSH is D013313.

My favorite is the MeSH style, because it allows for the most information to be conveyed succinctly. You should use numbers of width 6 or 7, even if you only plan on curating a few dozen or a hundred terms.

Please don’t use the GO style identifiers, because this creates a ton of confusion.

You should also write down what the regular expression that goes with your identifiers for later validation. In the MeSH example, the regular expression is ^(C|D)\d{6,9}$, which means it either starts with C or D, and is followed by between 6 and 9 numbers. The ^ means beginning of the string and $ means end of the string, so it’s clear that nothing can precede or follow.

Pick your scope

The last, and most important, part of planning is to pick the scope of your ontology. You have to choose what kinds of entities you want to include (genes, proteins, side effects, etc.). Keep in mind that if you’re picking one of these examples, there’s probably already a good nomenclature source for it, so it’s best you don’t curate it again.


It’s time to start curating entries in your ontology. Most people go right to Protégé.


Protégé is a perfect way to get bogged down in the ivory tower that is ontology. Instead, it’s better to focus on the aspects of the ontology that I think are practically the most important. So in this guide, we’re going to use a set of interconnected tab-separated values (TSV) documents. Why TSV? Because comma-separated values (CSV) documents look awful and Excel sheets can’t be diff’d / viewed in GitHub. However, I would accept the following alternative:

In a later post, I’ll come back to how to programatically generate OWL, OBO, BEL, and other formats that are commonly used for ontologies from your curation sheets.

Curate entities

The most important thing in an ontology is the entities. Make a file called entities.tsv. It needs a few columns to hold the most important information for each entity:

  1. Identifier - the identifier of the entity
  2. Name - the preferred name of the entity in the main language of the ontology
  3. Type - the entity type. For example, the Gene Ontology has three entity types - biological process, cellular component, and molecular function (they call them “namespaces”). This isn’t the same as the parent of the entity
  4. References - keep a comma-separated list of CURIEs pointing to resources that have more information about this entity from PubMed, PubMed Central, etc.
  5. Description - a short description of the entity written as prose. Shamelessly borrow from Wikipedia if appropriate, but remember to cite your source!
  6. Curator ORCID identifier - it’s really important to keep track of who added entries to the ontology so you can get in touch when there is confusion or errors are found. The ORCID identifier is the best unambiguous way to do this.

You should write and enforce a style guide (e.g., only proper nouns are capitalized in labels for entities) for names and definitions while you’re here.

If there are other pieces of information that all entities must have in your ontology, then you can also include it in this sheet. Later, the properties and relationships sheets can be used for other information and other relationships such as parent/child relationships, physical properties, etc..

Curate synonyms

Make a file called synonyms.tsv. It needs a few columns to describe synonyms for each term and the provenance of where they came from:

  1. Identifier - the identifier of the entity that matches to the entities.tsv sheet
  2. Synonym - the actual text you found
  3. Provenance - a CURIE describing the source that had the synonym. This might be a PubMed, PubMed Central, URL, or related.
  4. Synonym Semantics - is this an exact synonym, a broad synonym, a narrow synonym, or a related synonym? Each entry should only be one of EXACT, NARROW, BROAD, or RELATED as defined in the OBO 1.4 standard. If you’re not sure, just put EXACT.

Curate xrefs

Make a file called xrefs.tsv. It needs three columns:

  1. Identifier - the identifier of the entity that matches to the entities.tsv sheet
  2. Xref Prefix - the prefix for the data source that describes the same entity
  3. Xref Identifier - the local identifier of the entity in the xref’s data source

The prefix and identifier for the prefix are split to avoid the headache of parsing CURIEs later.

It’s best to consider xrefs as equivalences. All other relationships should be in the relationships page (later).

Curate typedefs

An xref is a very specific type of relationship, so it has first-class status. The parent-child relationship is also first-class and it goes without saying. The rest of the relationships (that can be expressed realized as triples) need to be either pulled in from a previously existing ontology like the Relation Ontology or PSI-MI, or defined in a structured way. Make a file called typedefs.tsv, to borrow from the OBO nomenclature for defining relationships. It should have the following columns:

  1. Prefix - could be the same as the current ontology, or an external one
  2. Identifier - if it’s from the current ontology, you might consider using a different identifier scheme than for entities. For example, WikiData uses ^Q\d+$ for entities and ^P\d+$ for relationships
  3. Name - The preferred name of the relationship
  4. (Optional) Inverse Of - If the relationship can be defined as the inverse of another, put its CURIE here
  5. Parent(s) - a comma-separated list of the relation’s parents’ CURIEs. This is a special case that doesn’t appear in the relationships sheets because isA relationships are so important.

Curate relationships

You’re ready to use the relationships defined in typedefs.tsv to write out relationships. Make out_relations.tsv with the following columns:

  1. Identifier - the identifier of the entity that matches to the entities.tsv sheet
  2. Relation Prefix
  3. Relation Identifier
  4. Target Prefix
  5. Target Identifier
  6. Target Name (Optional, but useful for readers)

Similarly, make another sheet called in_relations.tsv with the following columns:

  1. Source Prefix
  2. Source Identifier
  3. Source Name (Optional, but useful for readers)
  4. Relation Prefix
  5. Relation Identifier
  6. Identifier - the identifier of the entity that matches to the entities.tsv sheet

Between these two sheets, you can encode relationships between entities in the ontology that are both incoming and outgoing, removing the need to define ad-hoc inverses of common relationships, like isA.

Curate properties

Properties are like relationships that point to scalar values instead of other entities. For a counterexample, synonyms are a first-class property that contains lots of extra metadata and therefore get their own sheet.

The rest of the properties will appear here. A good example of a property is the chemical formula, SMILES string, and mass of a given small molecule in the ChEBI ontology. However, not all entries in the ChEBI ontology are small molecules, so if they were following this guide, it might not have made sense to put that property in the entities.tsv sheet. Make a sheet called properties.tsv with the following columns:

  1. Identifier - the identifier of the entity that matches to the entities.tsv sheet
  2. Property - it’s up to you how to decide what the properties in your ontology are. It’s not as common to define it as precisely as with relationships
  3. Value
  4. (Optional) Data Type - the XSD data type of the value for the property. If this isn’t important to you, your life will probably be better by leaving it out

Now that you’ve made all the sheets, you can make sure that your curators do their best job to fill out entries in each of them every time a new entity is added. It’s also necessary to keep track of the uniqueness of entity identifiers as new ones are added. It’s best if they’re consecutive and increasing, too.


One of the other real dangers of starting your own ontology is the entire concept of maintenance and quality assurance. If you’re working in an academic group, it’s highly unlikely that you will have the resources, motivation, or willpower to maintain the ontology that you are building. This can be proven by reading through the ontologies listed in BioPortal. While this might be unavoidable, there are a few things that you can do before your time as a PhD student, Postdoc, or whatever comes to an end to make sure that your ontology is actually useful for somebody else.

I’ve already given my explanation of why to use TSV - it makes sure there’s no conflicts with spaces or commas, tabs never show up in real text, and GitHub will make nice renders of TSVs and show you the diffs as versions change, versus Excel documents, which are saved as binary.

Version Control

As I’ve just alluded, use version control. Keep track of how your ontology changes over time by making a repository on GitHub. I’ve heard rumors that git was created by Linus Torvalds to slow people down, so you should use an interactive GUI for git like GitHub Desktop (you’re not a martyr!). While you’re working on GitHub, you should use the GitHub Flow workflow, which involves forking (or branching), making pull requests, then reviewing and discussing before merging into master. This is more relevant for people who are working on teams. If you’re not working on a team, try pulling in a collaborator to review your work as a pull request. Or email/tweet me! I’d be happy to help if you’re working in open source with a publicly usable license.


The next few suggestions rely on a bit of technical expertise. The first is that you should write scripts that validate the content’s integrity, formatting, correctness, or whatever rules you can come up with. Then you should use continuous integration (e.g., Travis-CI or GitHub Actions) to run those scripts on every commit to give feedback. If you’re working using the GitHub Flow fork/pull request workflow, then you can always ask your curators to make sure that their content doesn’t make the validation scripts fail before merging them into master.

Next, you should write scripts that export all of your content into common formats so others can consume it like OWL, OBO, BEL Namespace, etc. Additionally, it’s nice to automatically build a website that displays all the curated content and allows people to explore it. GitHub will even host the site for free.

These suggestions probably sound a bit abstract or scary if you’re not a seasoned programmer, so in a later post, I’ll provide you with a cookiecutter template repository with all the files, scripts, and configuration that you need to do this without any programming at all. An example of most of it in practice is the Curation of Neurodegeneration Supporting Ontology (CONSO) (source code; web site). It has a few differences from the recommendations I’ve made in this post - some of them inspired by choices I made during the curation of CONSO that I think could have been done better.

Choose a License

The license tells other people how they’re allowed to use your ontology. If you don’t use an appropriate open license, other people will not be legally allowed to use your ontology. And if that’s the case, there really wasn’t a point to making it (yes, I’m being pedantic here). Check out https://choosealicense.com/non-software/ for some pointers. I suggest the CC0 license, which is the most usable one out there. Don’t fear - people will cite your work and thank you for it, even if the license doesn’t legally obligate them to.

Making Releases and Long Term Maintenance

If you’re using GitHub, you can easily integrate the repository with Zenodo, which archives the repository when you make a tag and assigns a digital object identifier (DOI) to each release. You might also want to make releases to the OBO Foundry or BioPortal. You might also want to register your prefix at Identifiers.org to give your CURIEs maximum legitimacy.

Even with the best intentions, you will inevitably have to change some names over time. This is okay because your identifiers are persistent! However, you might have to retire entries. This might mean adding a column to entities.tsv with the date that a term is made obsolete.

Upgrade to more powerful tooling

While this guide was focused on how to get started with building ontologies if you’re new, there are obviously a lot of good reasons why people use more powerful formalisms like OWL to curate their ontologies. Chris Mungall, a prominent ontologist, gave me some feedback via Twitter that’s definitely worth a ready from anyone who made it this far in the following thread:

To summarize, he linked to the following article to show how OWL enables the Gene Ontology to do many more powerful things, such as axiomization, that just don’t fit into a TSV-only world for curation.

Use of OWL within the Gene Ontology Christopher J Mungall, Heiko Dietze, David Osumi-Sutherland. bioRxiv, 010090; DOI: 10.1101/010090

He also gave two links to some previously developed tools that can help you get started with generating ontologies from tables:

  • https://github.com/INCATools/ontology-development-kit/
  • http://robot.obolibrary.org/template

These accomplish a similar goal to what I’ll present in the next blog post, and I’ll have to do my due diligence to figure out how they work to provide a more in-depth comparison.

You can’t compete with UniProt, the Disease Ontology, the Gene Ontology, or other groups that exist to maintain high quality resources.

So don’t.

Join them.

Make sure that your ontology is written well so the relevant parts can be incorporated into these and other high quality, maintained ontologies. Then, get in touch with their maintainers. Tweet at them, send GitHub issues, etc. They’ll be happy to get input on what they should do next, because, like I said before, these people love what they do. And there’s nothing better than seeing that something you are proud of is useful for other people.

Stay tuned for my next post where I’ll give you the code I wrote to do all the things I recommended before. I’ll put my money where my mouth is and present my ontology that led to building this curation environment and ultimately writing this post - the Curation of Neurodegeneration Supporting Ontology.