Generative AI for Creating Multilingual Taxonomies

Taxonomies enhance the capability to identify and retrieve desired content.  Automated translation (machine translation) expands the scope of access to information to content in other languages. Applying automated translation to the contented identified using taxonomies thus enables people to find content on the subjects they want and then have it translated if needed. To retrieve such multilingual content, however, a taxonomy must also be multilingual. Concepts in the taxonomy must have names/labels in different languages: the language of the user and the languages of the content so that it can be tagged to the content.

Creating multilingual taxonomies

Fortunately, the data model upon which most large taxonomies are being built and managed supports multilingual concepts. The data model standard SKOS (Simple Knowledge Organization System) supports bilingual and multilingual taxonomies, because it models “concepts,” not “terms,” which has been referred to as “things, not strings,” and concepts can have any number of labels (strings of text) to describe them: a preferred label in each language that is displayed and any number of alternative (variant) labels in any languages, which are not displayed but match user searches and text strings for tagging.

The fact that multilingual taxonomies are simple to support technologically does not mean that they have been easy to create with translations. Whether translation is done by human translators or by machine translation software, translating a taxonomy is not as easy as translating narrative text. Both human translators and automated translation tools, (using rule-based, statistical, or neural network AI methods) look at complete sentences and not just isolated words. Words may have different meanings depending on their use in a sentence, and the context of a sentence may call for a different synonym in the translation target language.

Taxonomies with their hierarchical relationships provide context for the meaning of their concepts, but this is not the same kind of sentence-based context that machine translation programs utilize. Human translators can look at the context of the taxonomy hierarchy, but it’s a much slower task than translating sentences (which can also use machine-assisted translation tools) and requires an understanding of taxonomies.  Relying on machine translation or human translators who don’t understand the purpose or subject area of the taxonomy can lead to errors in translating taxonomies.

LLMs for generating multilingual taxonomies

Now, AI, in the form of LLMs, has become available to help generate taxonomies (which still require human review). LLMs don’t just generate terms, but they generate the correct hierarchical relationships to other concepts, multiple labels (as synonyms) for the same concept, and even definitions for concepts.

LLMs go beyond traditional machine translation and take the existing hierarchy into account when generating a translated label for a taxonomy concept. LLMs also follow specific instructions (or “prompts”) regarding the purpose and nature of the taxonomy. LLMs can also be instructed to focus on the meaning of the concept rather than on a literal translation of the preferred labels and each of the alternative labels. While the preferred labels for a concept in different languages are close translations, the alternative labels are not translations of each other but rather refer back to the concept. Even the number of alternative labels for a concept will vary by language. Finally, definitions are generated based on the concept and not as translations of existing definitions.

I explained this in a little more detail in my prior blog post Generative AI and Taxonomies for Finding Information

LLMs for generating new taxonomies in different languages

LLMs can be trained on, access, and generate text in different languages. This means that LLMs can generate entire taxonomies or parts of taxonomies in different languages from the start (with appropriate human-created prompts), without having to translate an entire existing taxonomy into another language.

Although a monolingual taxonomy does not have the same benefits of retrieving content in multiple languages as does a multilingual taxonomy, sometimes a monolingual taxonomy is desired. If a taxonomist is not available to create an entire taxonomy in the language, and a taxonomy on the subject already exists in another language, then translating a taxonomy has been a method to create a monolingual taxonomy in another language. As previously explained, translation of a taxonomy is not ideal and is prone to errors if not done by a translator-taxonomist. Using LLMs with the involvement of a subject matter expert (not necessarily a taxonomist) will generate a better taxonomy than a translation.

When using AI to build a taxonomy, it’s still best to have the top level developed manually to serve the specific use case, to create certain branches manually that are specific to an organization, and to generate incrementally those parts of the taxonomy built with AI, with the subject matter expert approving/disapproving suggestions for concepts and alternative labels at various stages. What is significant is that a person who is a combined taxonomist/linguist/subject matter expert is not needed to create a taxonomy in each language. In this way, taxonomy creation becomes easier to do globally. 

LLMs and multilingual generation in taxonomy management software

Managing a taxonomy in taxonomy management software has many benefits, especially when managing multilingual concepts. Now taxonomy management software is beginning to include support for LLMs to generate parts or all of a taxonomy, and thus support is integrated into the taxonomy creation and editing workflow. One tool, Graph Modeling from Graphwise, has now (last month) added the feature taxonomy generation in different languages to its Taxonomy Builder generative AI component. You can generate a monolingual taxonomy in any configured project language, and you can generate additional language versions of a taxonomy (creating a multilingual taxonomy) by generating concept preferred labels, alternative labels, and definitions, through LLM generations, not as direct translations. This essentially eliminates the need to translate taxonomies.

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 GenAI 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.

 

Taxonomies can also improve the reliability of AI agents. They have been used to provide context for Copilot agents on an organization's terminology. Additionally, SharePoint Knowledge Agent uses AI-assisted metadata extraction to automatically classify unstructured content and map it to an organization's taxonomy.

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 at some point 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.

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!

Generative AI at Taxonomy Boot Camp Conference

Generative AI and large language models (LLMs), the technology behind ChatGPT, have been topics of presentations, keynotes, and attendees’ conversations at all the varied conferences I had the fortune to attend this year, including the Taxonomy Boot Camp conference held November 6-7, in Washington, DC. Taxonomy Boot Camp is the only conference dedicated to taxonomies.

Opening and Keynotes

 

Right from the beginning in the opening welcome, the conference chair Stephanie Lemieux mentioned uses of ChatGPT for taxonomy creation, such as asking prompts: What is a category for a following list of terms?, What label for a concept might be better for scientists, or better for parents?, and What are alternative labels for a specific content? It has become clear that generative AI is a tool to assist taxonomists with specific tasks of a project but is not appropriate for automating the entire creation of a taxonomy. Thus, the Taxonomy Boot Camp theme this year, “Humans in the Loop,” was quite apt for the new era of generative AI, even if not specific to it.

 

The Taxonomy Boot Camp opening keynote, “Ontologies in the New Age of AI” by Dean Allemang, was on this subject. Dean is more of an ontologist than a taxonomist, hence the title, but he discussed both taxonomies and ontologies. Allemang made the statement that Generative AI “understands” why we need a taxonomy (even if managers do not). He explained that Schema.org has put RDF on many websites, which ChatGPT “reads.” Allemang has found that ChatGPT also performs perfectly on SPARQL queries, the query language for data, including taxonomies, that is in RDF. Allemang gave ChatGPT query examples, such as “Return all the claims we have by claim number, open date, and close date,” and “What is the total loss of each policy where loss is the sum of loss payment, loss reserve, expense, payment, and expense reserve amount?” Allemang advised taxonomists to identify uses for taxonomies that have not been fully delivered on and use generative AI to deliver it, and if people argue that generative AI does not understand their language, taxonomists should build in a link to the taxonomy that makes generative AI understand it.

 

On the second day, Taxonomy Boot Camp registrants  attend the same shared keynote presentations with all of the KMWorld co-located conferences, and this year these mostly dealt with generative AI, including the opening keynote by Dion Hinchcliffe “Tech-Driven Enterprise Thrills & Chills: The Future of Work.” 

Regular Sessions

In addition to being mentioned in various talks, generative AI was also the subject of a session, “ChatGPT, Taxonomist: Opportunities & Challenges in AI-Assisted Taxonomy Development,”  which comprised two separate presentations.

In this session, Xia Lin presented in “Chat GPT and Generative AI for Taxonomy Development” in which he discussed the steps involved in using ChatGPT in two case studies. In one, a taxonomy for data analytics projects of a small business was developed by providing ChatGPT with the scope of the first level of the taxonomy and then asking ChatGPT to expand individual categories by adding subcategories and then to add definitions of terms and categories. The results were reviewed and revised by experts. But Lin did not stop there. He showed the results of asking ChatGPT to provide stakeholder interview questions around a category, and (for those more technically inclined) how to create a ChatGPT plug-in for various defined functions of taxonomy creation, using ChatGPT’s APIs. 

Also in “ChatGPT and Generative AI for Taxonomy Development” Marjorie Hlava and Heather Kotula jointly presented on issues of the use of ChatGPT to create taxonomies and in general. They explained the risks of bias, plagiarism, ethics, data quality, matching the generated taxonomy to the content, and the amplification of errors upon repeating a prompt. In plagiarism, for example, if you ask ChatGPT to return a complete taxonomy on a subject domain in may return a copyrighted taxonomy that cannot be reused without a license.

Generative AI also impacts the topics of other presentations. For example, in the presentation “In Taxonomy We Trust: Building Buy-In for Taxonomy Projects,” Bonnie Griffin mentioned the importance of “continually re-introducing the value of taxonomy, as generative AI captures attention.” It was also the subject of a debate question in somewhat humorous closing sessions “Taxonomy Showdown—Point/Counterpoint With Taxonomy Experts.”

 

More on Taxonomies and AI

Of course, there is more to AI than just generative AI. Other sessions dealt with machine learning for auto-categorization. These included presentations by each Bob Kasenchak and Rachael Maddison in the session “Machine Learning Is Coming forYour Taxonomy,”  (link to Bob’s slides)  and Wytze Vlietstra’s presentation of  “Vision for Modular Taxonomy Product at Elsevier,” in which the program included “shared infrastructure supported by AI-based decision support tools.” In fact, AI has been a theme of Taxonomy Boot Camp in the past, in 2018. It is generative AI based on large language models that is new. 

For some more details on how this technology may be used for taxonomy development, see my prior blog post this spring “Taxonomies and ChatGPT.”  To get another perspective on this conference, check out the recent blog post by Taxonomy Boot Camp speaker Mary Katherine Barnes “Integrating AI: Insights from KMWorld 2023.”

Taxonomies and ChatGPT

ChatGPT, generative AI, and large language models (LLMs) are hot topics of interest in fields of data, information, and knowledge management. LLMs dominated the keynote presentations at the networking conversations at Knowledge Graph Conference in New York and were also discussed in presentations and panels of this conference and Data Summit in Boston, both of which I attended this month. The technology is relevant to taxonomies as well.

ChatGPT is the user interface application on top of GPT (Generative Pre-Trained Transformer), a publicly available LLM developed by OpenAI, which is now in version 4. ChatGPT is thus a form of generative AI, in how it generates answers. There are many other LLMs (Neural network-based AI, trained with deep learning on very large volumes of text), including those which are proprietary, restricted, or for non-commercial research, but only some have generative AI user interfaces. Although we may think of generative AI for providing answers to questions, it can do a lot more, including tasks related to taxonomies.

Organizing terms into hierarchies

Building a taxonomy is a combination of top-down design (identifying the top concepts or facets) and bottom-up building (identifying specific concepts from content analysis). The top-level of a taxonomy is designed to serve user needs and thus should be based on stakeholder interviews, surveys, and brainstorming workshops, which is not something ChatGPT can do.  The bottom-up building a taxonomy, based on terms extracted content or search log terms, may benefit from some AI involvement.

I have made a few test requests of ChatGPT for “Put the following list of terms into a hierarchical taxonomy…,” and the results are bulleted lists with indented narrower concepts. ChatGPT can also generate a taxonomy in a machine-readable SKOS in a requested RDF serialization format, as Bob DuCharme explained in his May 20 blog post “Getting ChatGPT to turn a flat vocabulary list into a hierarchical taxonomy.”

Like card sorting exercises, you can specify the top categories/concepts (like a “closed card sort”), or you can let ChatGPT create the top categories (like an “open card sort”). In any case, better results are with context, of course, so you should also tell ChatGPT what the subject domain or context is. Asking for a hierarchical taxonomy results in a third level of hierarchy sometimes, and not just a single level of grouping. Near duplicates usually appear next to each other in the list, and the taxonomist can then decide if and how to merge them into a single concept.

It is particularly for long lists of terms, where automated methods can save the taxonomist’s time. If a taxonomist comes up with terms based on manual content analysis, stakeholder interviews, or submitted lists from subject matter experts, the term lists tend not to be very long, and even the process of coming up with the terms tends to include some thoughts toward categorization at the same time. Longer term lists (such several hundred) are derived from automated term extraction (using text analytics technologies) across a corpus of dozens or hundreds of documents and from search log reports. ChatGPT is practical for putting these long lists of terms into draft hierarchies. There are inevitably some taxonomic errors in the results, which should be obvious to any taxonomist. For example, I have seen duplicated terms on different levels of the hierarchy.

In both lists of extracted terms and search log lists, terms occur that are not suitable as concepts for a taxonomy, such as verbs and adjectives or vague words. ChatGPT understands grammatical rules, so my prompt also says “Include in the taxonomy only nouns and noun phrases and omit the other terms.”

Generating alternative labels (“synonyms”) for concepts

Asking ChatGPT to “provide a list of synonyms for…” a given term can also be helpful for coming up with alternative labels for taxonomy concepts. Alternative labels should be customized for the context of the content and users, so alternative labels for a concept will vary from one taxonomy to another, and an external source, such as ChatGPT should not relied upon as the only source for alternative labels, but merely as a supplemental source of suggestions to be considered. 

Again, context can help and should be provided. I asked “Provide a list of synonyms for “healthcare” and got 20 terms. But then when I asked “Provide a list of synonyms for health care, meaning the industry,” I received a slightly more focused list of 15 terms. Interestingly, the two-word variant “health care” was not on the list, so “synonyms” is understood by ChatGPT to mean different words with the same meaning and not orthographic variations. Nevertheless, even 15 terms are too many, and the taxonomist should select from the list of suggestions. It might be a good idea to then test search the suggested alternative labels in the content and system being used.

Although by strict definition a “synonym” is a single word with the same meaning as another word, ChatGPT provides acceptable synonyms for terms which are multi-word phrases, or synonymous multi-word phrases, such as “Chemical manufacturing and distribution” provided as a synonym for “chemical industry.”

Other taxonomy-related uses of ChatGPT

Getting help in designing an ontology (a more complex, yet high-level semantic model with defined classes of concepts, customized relationships, and attributes) is also possible with ChatGPT or other LLMs. Again, submitting the request multiple times with slight variations will yield multiple different responses for the ontologist to consider and select ideas from. Ontologies are not expressed in simple text, though, so the prompt request should specify it, such as RDF TTL. Dean Allemang, author of Semantic Web or the Working Ontologist, has written multiple articles (medium.com/@dallemang) recently on ChatGPT and ontologies/knowledge graphs.

ChatGPT can also be used for comparing lists of terms, data conversion, and basic coding, which may be useful for taxonomists who lack coding skills. It can convert taxonomy or ontology data from one data format to another (although taxonomy/ontology management software also imports/exports in multiple formats). Taxonomies and ontologies in their raw data format are most commonly expressed in the RDF (Resource Description Framework) data model which has various serialization format: RDF/XML, JSON, JSON- LD, .ttl (Turtle), etc., and ChatGPT can convert data from one to another. Data extraction can also be done with ChatGPT. For example, knowledge management professional Camille Mathieu recently shared in a LinkedIn post how she used ChatGPT to write a Python script to extract text & metadata from PDFs.

Perhaps what is most intriguing as a future implementation of taxonomies and ChatGPT is to go in the other direction and have knowledge organization systems, such as taxonomies, support the creation and use of queries (as called “prompts”) for generative AI, to obtain better results. This requires some back-end development, though, and is not merely a matter of putting a taxonomy into a prompt.  Since a taxonomy is created for a specific subject domain, the questions need to be confined to the domain of the taxonomy. Semantic Web Company has developed a simple publicly accessible demo “PoolParty Meets Chat GPT,” whereby you can compare the results of questions you ask in the subject area of ESG (Environmental, Social, and Governance) that are submitted directly to ChatGPT and with those which are filtered through an ESG taxonomy and knowledge graph (managed in PoolParty software) so that the questions are enriched before being sent to ChatGPT. The semantically enriched questions generate answers that have more detail, better accuracy, and even web links to definitions and other articles.

Conclusions

While it’s arguable whether ChatGPT alone is a good way to obtain “facts,” there is no doubt that it is a good way to get suggestions and ideas. These suggestions can support the work of taxonomists and ontologists, and taxonomies and ontologies in turn can support the results of ChatGPT and other LLMs. Because there will be errors from ChatGPT, it should not be used to generate taxonomies by those who are not already knowledgeable with taxonomy requirements and best practices, nor should it be used as a substitute for the expertise of taxonomists.

I hope to experiment more with ChatGPT for taxonomies and share additional details in future blog posts.