Generative AI and Taxonomies for Finding Information

Generative AI (GenAI) and Large Language Models (LLMs) have provided numerous solutions in many applications. The original public application of question-answering, popularized by ChatGPT, has become ubiquitous and has changed the way people search for information on the web and more recently inside the enterprise as well. Instead of keyword searches, people are submitting full sentence questions that can be refined. Full sentence queries allow for complex questions beyond just retrieving information “on a subject.” The system’s chat-type responses with suggestions on how to refine the query are especially useful and have improved the user experience of conducting searches and getting results. 

The question has arisen: “Why do you still need taxonomies and semantic tagging when AI could do all of that automatically?” Although GenAI has improved the experience in getting answers to questions, the accuracy and consistency of the results can be lacking. As taxonomies have enhanced traditional enterprise search results, taxonomies can also improve GenAI query results.

 

 

The Value of Taxonomies in Search and Findabililty

In the digital space, search engines at first seemed to compete with taxonomies, but soon it became apparent that search alone had short-comings. The same term with multiple meanings or the negation of a term results in false search results. On the other hand, the existence of synonyms for the same concept results in not retrieving (missing) desired results that are described with a different synonymous term. Furthermore, the search box by itself does not allow users to refine or expand their search results. 

Taxonomy concepts and semantic tagging for what content is about, not merely the mention of text strings, achieve better search results. Taxonomies bring together different synonyms or names of the same thing. Additionally, the display or partial display of taxonomies, as browsable hierarchies, filtering facets, or term matches to search strings in drop down (sometimes type-ahead) lists, have given users more control over search and more confidence in the results. Hierarchy can also be utilized in information retrieval, whereby a concept retrieves not only the content it has been tagged to but also the content that has been tagged to each of its narrower concepts. 

The Value of Taxonomies in Supporting Generative AI and LLMs

Like search, GenAI can be implemented without taxonomies, but combined with taxonomies for an enterprise implementation yields better results. LLMs work with patterns and predictions, and they do not always resolve synonyms. If you query “What are the leading U.S. pharmaceutical companies?” and “What are the leading U.S. drug companies?,” you don’t get identical results, although the answers are similar. 

When querying internal, enterprise information or data, a higher level of accuracy is expected and needed, and synonyms need to be made explicit. This can be done through a taxonomy, which the LLMs can reference when Retrieval Augmented Generation (RAG) is implemented, which reduces hallucinations, contradictions, and inconsistencies. 

Other taxonomy features than the synonyms may also be leveraged with RAG. Relationships between concepts (broader, narrower, and related) in the taxonomy can extend the retrieval. The hierarchy feature of a taxonomy also serves LLMs by providing context and thus more specific meaning for concepts through their hierarchical relationships. Furthermore, there may be terminology used uniquely to the enterprise, which an LLM wouldn’t know, such as “active customer.” Adding definitions to taxonomy concepts is useful both to the LLMs and to the human users. 

Data-heavy organizations that implement LLMs internally combined with a custom taxonomy at the enterprise level usually opt to go a step further and implement GraphRAG. GraphRAG combines LLMs with a knowledge graph, which comprises a taxonomy, ontology, and linked instance data in a graph database. This way, the LLMs can make use of the explicit semantic relationships in the knowledge graph, which support complex, multi-component queries. Because of their support for GenAI and LLMs using RAG or GraphRAG, taxonomies and semantic tagging are more relevant than ever.

Using Generative AI to Create Taxonomies

The next obvious question is “Can you use GenAI and LLMs to generate taxonomies?” Yes, you can. Taxonomies, however, are more complicated and nuanced than they might seem. Taxonomies should be customized to the content and data they will be used for, the end users’ needs and expectations, the use cases or purposes they will serve, and front-end application requirements. You would need to provide very detailed and lengthy prompts just to get started.

The best approach is to use GenAI for a taxonomy selectively. You may generate selected parts or branches of a taxonomy (such as topics, trends, technologies, or regulatory framework), but not for an organization’s own products, services, departments, or offices. 

GenAI is also suitable for various sub-tasks of taxonomy creation, such organizing a flat list of terms into a suggested hierarchy, suggesting alternative labels (synonyms) for a concept, suggesting narrower concepts for a concept, generating definitions for concepts, or explaining the relationship of two technical concepts to each other (broader/narrower inclusive, related and overlapping, or synonymous).

You can also use GenAI to generate a suggested starter taxonomy to use as a source for ideas and inspiration without adopting most of it. In any case, the specialized role of custom taxonomy should always involve human-the-loop interaction, instructions, review, and editing.

Taxonomy management software vendors are beginning to incorporate LLMs into their products to assist with the auto-generation of taxonomies or parts of taxonomies that their software manages. The vendor with the most advanced feature is Graphwise. I’ve had the opportunity to try out the Taxonomy Builder feature, which is integrated into Graphwise's Graph Modeling taxonomy/ontology management tool. You can read more about it in “How AI and Taxonomy Builder Support the Building of Taxonomies.”

Using generative AI to assist in the creation of custom taxonomies accelerates the process and supports taxonomy best practices with which project owners or subject matter experts may not be familiar. It also helps skilled taxonomists create taxonomies in subject domains in which they lack expert knowledge.

The Role of Taxonomists with AI

AI has led to the decline in certain information management jobs but not others. The role of professional content indexers has definitely declined with AI (not even GenAI) over the past decades. I know, as I used to be an indexer. Human tagging as a task, not a job role, continues to a limited degree, but increasingly the task involves reviewing and accepting/rejecting automated tagging suggestions.

The role of taxonomists will probably not decline, but will change. The need for taxonomies is growing. With GenAI, professional taxonomists are able to create taxonomies faster, so the cost of taxonomy creation is going down. (The LLM subscriptions are already being paid for other enterprise uses.) The use of GenAI to help create taxonomies also make their creation more feasible for those who are not taxonomists. Experienced taxonomists are still needed to provide initial guidance and ideally review and feedback. 

The role of taxonomy consultants, as myself, will likely also change. Instead of taxonomy project consulting engagements that last many months with intensive information gathering and numerous stakeholder interviews, followed by manual taxonomy creation with iterative reviews, more consulting engagements will involve helping design the start of the taxonomy, guiding clients to use AI, providing feedback, and developing the taxonomy governance plan.

Taxonomists are identifying more ways to utilize GenAI in their work. I will write another blog post on that in the future, and I will be chairing a panel of taxonomists using GenAI at the next Taxonomy Boot Camp conference in Washington, DC, November 16-17, 2026.

Is a Taxonomy an Ontology?

At last month’s Knowledge Graph Conference, in addition to knowledge graphs and graph databases, there is a growing interest in ontologies, but the role of taxonomies does not seem so well understood. For example, in one presentation I attended, it was said "you get synonyms/alternative labels into a knowledge graph via ontologies," rather than mentioning taxonomies. More than one person asked me: isn’t a taxonomy a kind of ontology?  

The fact that, technically, SKOS (the data model for interoperability used for taxonomies) has been designed as upper ontology, can lead to the conclusion that all taxonomies modeled on SKOS are then domain ontologies, as they are instances of the SKOS upper ontology. However, that is a more theoretical way, than a practical way, to look at taxonomies.

When I write or speak about taxonomies, I aim to be practical. While theoretically a taxonomy is a kind of ontology, in practice it is not, and maintaining a distinction helps clarify how each a taxonomy and an ontology can improve on each when they are combined.

If you are an ontologist and see everything through the lens of ontologies, then you probably consider that a taxonomy is a simple type of ontology that merely does not utilize all the features of a full ontology. If an ontology is simply defined as a knowledge model that has classes (things), relationships between the things, and attributes as properties of the things, then, yes, a taxonomy is a kind of ontology. It has concepts, hierarchical relationships, and often other attributes for concepts, that typically merely definitions, scope notes, or other notes.

The problem of calling any taxonomy an ontology is that the benefits of semantically enriching a taxonomy with an added ontology or extending an ontology with a taxonomy might not be well understood. We add an ontology to a taxonomy in order to provide customized semantic relationships and attributes of all kinds. Additionally, basing the added ontology on OWL (Web Ontology Language) enables capabilities of inferencing and reasoning.

Furthermore, saying that a taxonomy is an ontology could lead to less than sufficient attention to the taxonomy features that ontologies alone lack. These features include alternative labels and hidden labels that match variants in both tagging and user searching, equivalent foreign language labels for concepts, concept schemes that can be implemented as search facets, and distinct fields for definitions and different kinds of notes that are standardized for interoperability.

If following the Semantic Web’s stack of data model recommendations, then a taxonomy can be defined as what is built on SKOS (Simple KnowledgeOrganization System), and an ontology is defined as what is built on RDFS(RDF-Schema) and OWL (Web Ontology Language). I find that a very clear explanation of the difference between taxonomies and ontologies to those who are familiar with ontologies. These different data models may be integrated within the same knowledge model, and that’s how we get taxonomies extended with ontologies or ontologies extended with taxonomies.

We might call taxonomy-ontology combinations “knowledge models” or “semantic models.” If the model has mostly taxonomy (SKOS-based) data, such as a large taxonomy with a little ontology added, it is best called a taxonomy, and if the model has mostly ontology (RDFS and OWL-based) data, such as a large ontology with some taxonomy  data, it is best called an ontology.

The organizers of the Knowledge Graph Conference understood the distinct role of taxonomies in knowledge graphs and thus welcomed me again to present a tutorial specifically on taxonomies.

Hierarchies and Attributes in Taxonomies

One of the challenges in creating hierarchical taxonomies is that there can be multiple ways to categorize concepts and thus design hierarchies. There are multiple methods to deal with this, including polyhierarchy and facets. Now that taxonomies are more often extended with ontologies, attributes can also be used for additional “classifications” of things.

Dealing with multiple hierarchies

The traditional method of dealing with multiple methods of categorizing concepts has been to put the concepts into a “polyhierarchy,” which means the concept has more than one broader concept, and thus belongs to more than one hierarchy.  The occasional polyhierarchy is acceptable, but if a polyhierarchy becomes extensive (numerous concepts belong to the same two hierarchies) due to different methods of classification, this does not serve the purpose of helping users find the concepts and tagged content desired. When everything is in a polyhierarchy, the guiding purpose of a hierarchy gets lost.

When the issue is multiple classifications for things, then what is known “faceted classification” is often the answer. A faceted taxonomy design involves designating a facet for each method of classifying things by. For example, products may have facets for brand name, product type, functional use/application, industry market, user type, etc. Each of these could be a facet for products.

Sometimes, however, there may seem to be more possible ways of organizing or classifying something than are practical for facets. It could be within a facet. For example, if you have a facet for product type, you could further classify the product types by product family, by  generic product type (narrower “is a” sub-type of the broader), by broader system of which they are a component (narrower is a part of the broader), by size, or by a certain key feature or characteristic.

Recently on a project, a client suggested an added level of hierarchy within the facet for named product models for a classifying feature that impacted the product size. The problem was that this would combine named entities (proper nouns) of product models and generic types within the same facet. This combination should be avoided in facet design, because facets enable users to search and filter by different methods, such as either by name or by type, and there are scenarios when users would choose one over the other. Combining types and named entities in the same facet can cause confusion. This is where an ontology model may be the solution.

Ontologies for further classification

Ontologies enable customized relationships between classes (which tend to be the same type of high-level grouping as a facet) and customized attributes for members of classes. When we think of ontologies, we usually think of the custom relationships, but custom attributes can support what could be considered “types.” These “types” might have been extra hierarchies, and thus attributes provide a solution to the multiple classification problem. 

If multiple methods of hierarchical classification seem to be overlapping, you should consider making one or more attributes instead.  In my recent consulting case example, what the client originally proposed as top concepts for grouping product models (as a classifying feature impacting the product size), we decided would work better as an attribute of the product models. So, the facet would contain only named entity product models, and the hierarchy would be by model family only.

When an ontology is defined as a formal naming and definition of the types, properties and interrelationships of entities in a particular domain, we might think we have to define everything in the domain, and thus creating an ontology is a large, complex project. Often, what we need is only “some” ontology. While using the features, rules, and data model of an ontology, we need to define only the types, properties, and interrelationships that need to be defined for a business purpose.  This could be defining just a few custom attributes (properties) without even adding any custom relationships.  

More information about attributes in is my prior blog post. "Taxonomies and Attribute Data." 

Examples

In the prior example, the product model feature had originally been proposed for the hierarchy for the purpose of “grouping,” because users might want to look up the product models by that feature. If implemented in a knowledge graph, the attributes, managed in an ontology, will also support users looking up entities by their attributes.  So, the hierarchical design is not necessary.

Any “groupings” of named entities (by region, size, role, etc.), should be reconsidered as attributes of the named entities. Other examples are groupings of vehicles by engine type, which could have engine type as an attribute instead, or groupings of appliances by energy type, which could have the fuel type as an attribute instead. So, instead of Electric cars narrower to both Cars and Electric vehicles, Electric, Internal combustion, and Hybrid would be attributes for Cars. 

Conclusions

Shared data model standards based on RDF (Resource Description Framework) and the use of dedicated taxonomy/ontology management software that combines taxonomies with ontologies make this solution of using ontology features to resolve multiple hierarchies easy to attain. Instead of thinking that we could extend a taxonomy into an ontology in the future, we should be thinking of how to design a knowledge model now that best serves the body of knowledge and the users.

Taxonomy Boot Camp London 2026

I was thrilled to participate in the Taxonomy Boot Camp London conference, which was in-person in London this past month for the first time since 2019. A sister conference to Taxonomy Boot Camp in the United States, which has been running since 2005, Taxonomy Boot Camp London had been running “Bite- Sized” online editions of half a day three times per years since 2020, which had been so successful that they continued through last year. The online edition will continue now, once a year, scheduled next for October 7.

Taxonomy Boot Camp London continues to be successfully chaired by London-based taxonomy consultant Helen Lippell, since its first year. She summarized this year’s conference: “I pushed the boundaries of my own knowledge and got to see a huge range of talks by our wonderful speakers …. Our workshops gave attendees the perfect grounding in foundational concepts too.”

As taxonomies are a niche specialty, which are applied to other related fields, the Taxonomy Boot Camp conference is always combined (co-located) with other conferences operated by Information Today Inc. In the United States, this has always been with KMWorld (knowledge management) and additional co-located conferences. For Taxonomy Boot Camp London, from 2016 to 2019 the conference had been co-located with Internet Librarian International to bring in enough attendance to make use of the venue and catering, but the conferences were not similar enough in content or attendance, and did not share keynotes, exhibits, or breaks. This year, for the first time, a new conference of KMWorld Europe was launched, and Taxonomy Boot Camp was fully combined with it, sharing keynotes, meals and breaks, exhibit space, and registration options. This made a lot more sense, due to the overlap of taxonomies and knowledge management. Personally, I also enjoyed seeing knowledge management colleagues, in addition to taxonomy colleagues, at the conference.

Conference Sessions

The format of the conference was the same as in previous years. After a shared keynotes each day, the conference is run in two tracks each day. Tracks are not the same as Taxonomy Boot Camp (Washington, DC (Beginner and case studies, and in two tracks only the first day) but rather on loose themes, which this year were “Components of Successful Semantic Projects”; “Joining Up Data With Semantics;” “Getting the Most of Curating Content, Data, and AI”; and “Taking Structure to the Next Level.” It was difficult to decide what to attend, and I moved between tracks often. 

Taxonomy Boot Camp London differs from Taxonomy Boot Camp (DC) by including preconference workshop options on the afternoon before the main conference. There were four workshops to choose from in the single time slot, two for Taxonomy Boot Camp, and two for KMWorld. “Taxonomy Design Fundamentals,” which I taught, and “Finding a Forever Home: Governance, Ownership, & the Long-Term Care of Taxonomies” were the two taxonomy workshops. 

The keynote speakers, Ben Clinch on the first day and Noz Urbina on the second day, both were excellent in taking up different angles to the topic of AI in knowledge management, while also touching on taxonomy.

What was interesting about the conference sessions was the diversity of presentation subjects. While some provided the expected information on how to create good taxonomies (including my joint presentation with Joseph Busch on Thesaurus Standards for Taxonomies”) and others were case study applications of taxonomies, there were additional, different topics. Bob Kasenchak of Factor presented an interesting perspective of semantic layers as abstraction layers, Teodora Petkova of Graphwise presented on how to embed meaning and consistency in content to support knowledge graphs and shared understanding. Craig Johnson of Xemma presented on how research was done to obtain taxonomist-user input in designing a new taxonomy management system.

Connecting to other knowledge organization systems was a common topic, with presentations on the connections of taxonomies and ontologies by Steve McComb of Semantic Arts and Paul Appleby and Ravinder Singh both of Graphifi, the intersection of taxonomies and terminologies by Jo Chapman, and taxonomies as metadata by Yonah Levenson.

There were, of course, numerous sessions on AI use in taxonomy building. Ahren Lehnart spoke about the ways to identify the best concepts out of those being suggested by machine learning and LLMs.  Panos Mitzias of Squirro presented on how AI can help accelerate tasks like concept discovery, drafting structures, and enriching taxonomies, but success still depends on clear scoping, stakeholder engagement, and ongoing governance. Fran Alexander of Expedia presented on various considerations regarding the use of LLMs in taxonomy creation including, provenance, traceability, authoritativeness, context, and the use of multiple LLM agents. Fran, Bob, Kasenchak, and Stephanie Lemieux came together for an impromptu panel discussion on the use of AI in taxonomy creation (filling in for a cancelled speaker). They spoke on the various positive uses of AI and the ways in which AI was still not so good. I found this panel most interesting, so I decided to submit such a panel topic for Taxonomy Boot Camp in Washington, DC, this November

Sessions are not recorded, but most of the slides are available on the conference website. Ahren Lehnart also blogged on the conference themes. 

Conference Details

The joint conferences had a total of about 250 attendees, which compares with 170 for Taxonomy Boot Camp London only in the prior years. (It’s not possible to break out Taxonomy Boot Camp registrants only, since many chose a “all access pass” to both conferences.)  The international aspect was great, with representatives from 29 countries. 

For the first time, the London conference (Taxonomy Boot Camp and KMWorld jointly) had a nearby off-site networking drinks reception the evening after the workshops and before the main conference. The semi-enclosed rooftop bar was a great place to meet and mingle. 

The conference facility venue location was better than previous years, being in central London, close to the Tower of London. The only issue is that the conference organizers were not sure how many attendees to expect, so they were conservative with the space, which turned out a little tight. Although there was enough seating the conference session rooms (barely), the showcase area, which was also where breakfast, lunch, and break refreshments were served, became quite crowded at times. So, it was challenging sometimes to meet people and visit all the exhibitors at times.

The vendor showcase was larger, and had better dedicated space, compared to the former Taxonomy Boot Camp London in-person events. I recall the 2-3 vendors back then having tables just outside the conference room doors. The dedicated showcase space where breakfast lunch and coffee breaks were served was a benefit for the exhibitors. As the venue was in the basement level, excavated ancient Roman walls were on display behind the exhibits. More taxonomy/ontology software vendors were present than in the past: Graphwise (formerly PoolParty), Squirro (vendor of Synaptica), Graphifi (vendor of Graphologi), and a brand new entrant Xemma. The taxonomy/ontology vendors were mixed in with the knowledge management vendors without distinction, and it was good to have this cross-over to learn more about what is available.

Taxonomy Boot Camp in London and the United States

The scope of subjects and themes of Taxonomy Boot Camp London are the same as at Taxonomy Boot Camp in the United States, but the many of the presenters are different with different case studies and stories to tell, and those presenters who are the same (like myself) do not give the same presentations at both conferences. The attendees (delegates) are also different. So, if you're just getting started with taxonomies, either Taxonomy Boot Camp London, or Taxonomy Boon Camp in Washington, DC, whichever is more convenient, is appropriate. If taxonomies are your profession, then you should try to attend each conference at least once. It’s worth the trip. I am looking forward to Taxonomy Boot Camp London / KMWorld Europe next time in April 2027.

Helen Lippell reflected: “I thoroughly enjoyed seeing the event come to fruition after all the hard work the team put in over the last year, and one of my abiding memories will be walking around after the last sessions seeing everyone just chatting away while the venue staff tried to tidy up! I take this as a sign of our community being in rude health and ready to grow in future years.”

 

 

 

Taxonomy Sources: Re-Used, Licensed, or AI-Generated

As a taxonomist, I often write about creating taxonomies from scratch, but in practice, many organizations often obtain at least some taxonomies or controlled vocabularies from other sources.  Although internal content about an organization’s business, products, or services requires mostly custom taxonomies, some taxonomies, such as for regions or technologies, may come from other sources. Content that comes from external sources, such as research articles, is also be appropriate for tagging with taxonomies from other sources.

For “other sources,” these could be:

• Governmental agencies or nongovernmental organizations which publish taxonomies, thesauri, and subject heading schemes for their purposes but which are freely available

• Companies which sell their taxonomies

• Taxonomies that are generated by AI

Taxonomies for Re-Use or License

Types of taxonomies available can be categorized in multiple ways that overlap:

•  available for free or for a fee
•  available for commercial re-use or not available for commercial re-use
•  permissible for modification or not permitted to modify
•  designed a created for a specific content set or intended for broader use

I had previously blogged on taxonomies for license, discussing the issues of fees, availability for re-use, and permission for modification. Now I want to focus on the issue of using a taxonomy created for a specific purpose. 

Recently, I worked for a client that had created taxonomies for the life sciences industries with sections based on branches on the National Library of Medicine’s Medical Subject Headings (MeSH), because it was free. MeSH, however, had been designed for indexing medical research literature, and turned out not to be suitable for my client’s purpose of helping biomedical and pharmaceutical companies find articles relevant to their business and market.

For example, MeSH organizes drug types by their chemical types (Heterocyclic Compounds, Enzymes and Coenzymes, etc.). For a biomedical drug discovery company or a pharmaceutical company, however, the focus and classification of drugs is instead based on what kind of disease they treat (Cancer Drugs, Alzheimer’s Drugs, etc.). Thus using concepts from MeSH is not so suitable for pharmaceutical industry taxonomy.

Previously, I worked at Gale, which developed and managed many controlled vocabularies (or taxonomies) for indexing periodical and reference literature, which it sold to libraries. For a time, Gale also offered for license subject-domain subsets of its subject thesaurus of over 10,000 preferred terms. I realized that the business terms to index articles in business news sources were not necessarily the same terms that a company would want to tag its business documents and intranet pages. Others seemed to realize this too, and Gale didn't sell any stand-alone taxonomy licenses as long as I worked there. 

Taxonomies that are designed purely for sale and not designed with specific content and user type in mind are more suitable for licensing and re-use. I’ve seen a few small scale examples of this with sets of keywords for sale for tagging photos. The only commercial business I am aware of that licenses full taxonomies (with alternative labels and multiple hierarchies) in various business and industry domains is WAND. These taxonomies, which are also enriched with alternative labels (synonyms/variants) are a decent way to get started. The taxonomies can then be edited or supplemented as needed. WAND taxonomies, which are manually developed, are particularly useful for product and services categories in various industries.

AI-Generated Taxonomies

When I first explored the use of GenAI to create taxonomies (described in my prior blog post), I felt that the results were quite inadequate, as LLMs were pulling from multiple sources, where the same term could have different meanings in different contexts, different terms could refer to the same thing, and even the hierarchy would vary for different use cases.

More recently, I’ve used ChatGPT and Claude and found that the results, especially when focused in areas of science, technology, and medicine, have improved with respect to specific taxonomy hierarchies. Even when I did not ask for a taxonomy, the LLMs often return respectable three-level hierarchies of concepts in such topic areas as medical devices, drug types, and cell receptors. I also found AI tools useful for disambiguating similar terms or providing synonyms for technical terms I was not sure of. 

AI-generated taxonomies are a potential competitor to WAND’s taxonomies for sale, but this depends on the size and subject area. The WAND taxonomies are large and detailed in the number of concepts, hierarchical levels, alternative labels, and they have already been expertly created by humans. Using AI to create taxonomies works better on single hierarchical trees, and always requires human editing to refine and complete the taxonomies. Hierarchies and alternative labels are created in separate steps. For multiple smaller taxonomies or taxonomy facets, AI is likely the more practical option than licensing full taxonomies. 

So, it shouldn’t be a surprise that taxonomy management software is starting to integrate GenAI and LLMs to automate taxonomy creation. For example, Graphwise Modeling (formerly PoolParty) introduced a Taxonomy Advisor feature in 2024, which allows users to request suggestions for narrower concepts, alternative labels, and definitions. This month, Graphwise announced the additional Taxonomy Builder feature, which enables the generation of a complete taxonomy hierarchy. It can be used for small portions or larger portions of the taxonomy, as needed, and it’s convenient to have the capabilities within a single tool. It also takes care of the prompt creation, based on the existing hierarchy and the user-entered description of the taxonomy and any additional instructions. I do not create taxonomy hierarchies with AI tools often enough to become good at writing the best prompts, so I appreciate it when a tool helps with that. There will be more about this later, as I working on white paper and will be speaking in a webinar in April on GenAI/LLMs in taxonomy creation. 

When to use Other Sources

As mentioned previously, taxonomies published from external sources are best used for content from external sources. When it comes to AI-generated taxonomies, though, it’s not necessary to generate an entire taxonomy, hierarchy, or facet. AI methods are quite suitable for smaller components of a taxonomy, such as narrower concepts to a single concept. As such, AI uses in taxonomy development are more widely applicable, including for enterprise taxonomies. For example, AI could be useful for generating a list of document types for a document type facet, and then after review, those AI-suggested document types that are not applicable can be removed. The starter list of terms can get people thinking of what might be missing, which is easier than trying to come up with a list of terms from scratch. 

In conclusion, an AI-generated taxonomy, after human review and editing, is usually a better solution than a licensed taxonomy that was created for a different purpose, such as using MeSH for the commercial side of healthcare. A taxonomy that is partially generated by AI or fully generated by AI that uses multiple sources and appropriate prompts (such as what is built into Taxonomy Builder) is typically a better source than a taxonomy that was created for a specific and different use case or than a taxonomy whose license prohibits editing or commercial re-use. If you choose to generate taxonomies with AI, I am happy to offer my services to review and edit them!