Saturday, February 24, 2024

Faceted Classification and Faceted Taxonomies

I have argued before that a taxonomy is not the same as a classification system, despite the original meaning of the word taxonomy as a system for classification. (See the blog post Classification Systems vs. Taxonomies.) Modern taxonomies that are used to support information management and findability are more similar to information retrieval thesauri and subject heading schemes than they are to classification systems. Another type of classification, the method of “faceted classification,” however, does apply to types of taxonomies. I would not consider “faceted classification” as exactly a synonym, though, to “faceted taxonomy,” though, as not all faceted taxonomies are the same.

What is faceted classification?

Facets for jobs
Facet means face, side, dimension, or aspect. In this sense, facets are meant to mean aspects of classification. A diamond, an object, or a digital content item is multi-faceted. A digital content item (text document, presentation, image, video, etc.) has multiple informational dimensions or aspects to it and thus multiple ways to be classified.

Classification is about putting an item, such as a content item (document, page, or digital asset) into a class or category. If it’s a physical object (a book) it goes into a shelf of its class. In faceted classification, an item cannot physically be in more than one place, but it can still be “assigned to” more than one class. So, while the book itself can be on only one shelf, the record about the book can be assigned to more than one class.

Faceted classification assigns classes/categories/terms/concept from each of multiple facets to a content item, allowing users to find the item by choosing the concepts from any one of the facets they consider first. Different users will consider different classification facets first. Users then narrow the search results by selecting concepts from additional facets in any order they wish, until they get a targeted result set meeting the criteria of multiple facet selections. The user interface of faceted classification is sometimes referred to as faceted browsing.

History of faceted classification

The idea of faceted classification as a superior alternative to traditional hierarchical classification, whereby an item (such as book or article) can be classified in multiple different ways instead of in just a single classification class/category, is not new. The first such faceted classification was developed and published by mathematician/librarian S.R. Ranganathan in 1933, as an alternative to the Dewey Decimal System for classifying books, called Colon Classification (since the colon punctuation was originally used to separate the multiple facets). In addition to subject categories, it has the following facets:

  1. Personality – topic or orientation
  2. Matter – things or materials
  3. Energy – actions
  4. Space – places or locations
  5. Time – times or time periods

Although it was not adopted widely internationally due to its complexities in the pre-digital era, colon classification has been used by libraries in India.

In the late 20th century, digital library research systems based on databases enabled faceted classification and search, with different fields of a database record represented in different search facets. Users interacted with through an “advanced search” form of multiple fields. Faceted classification and browsing gained widespread adoption with the advancement of interactive user interfaces on websites and in web applications in the late 1990s and early 2000s. Thus, facets started being displayed in more user-friendly ways that were no longer “advanced.”

Structure of facets

It’s not necessary to follow Ranganathan’s suggested five facets, but that’s a good way to get thinking about faceted classification. Another way to look at faceted classification is to consider a facet for each of various question words: What, Who, Where, When

  • What kind of thing is it – content type
  • What is it primarily about - subject
  • Who is it for or concerns – audience or user group
  • Where is it for/applicable, or where it depicts (media) – geographic region
  • When it is about – event or season (not date of creation, which is administrative metadata, instead of a taxonomy concept)

The additional question words of “why” and “how” are relevant in some cases, but less common. An individual content item typically does not address all of these questions, but usually addresses more than one. When creating facets, most of the facet types should be applicable to most of the content types.  

Another good way to think about faceted classification is to put the word “by” after each facet, to suggest classification and filtering “by” the aspect type. A logical and practical number of facets tends to be in the range of three to seven.

A standard feature of facets is that they are mutually exclusive. A concept/type belongs to only one facet. This is typical practice for the design of classification systems. The difference is that in faceted classification it is merely the concept/type/term that belongs to just one facet, not the content item or thing itself that would belong to only one classification in traditional classification systems.

When a faceted taxonomy is not for classification

The design, implementation and use of facets to construct or refine searches has become so popular that it is no longer used just for classification aspects. Rather, a faceted taxonomy design may be used for any faceted grouping of concepts for search or metadata types that are relevant for the content and users.

Faceted classification is intended to classify things that share all the same facets. For example, all technical documentation content has a product, feature, issue, and content type, so these are faceted classifications. But with more heterogeneous content, facets are not universally shared. While the facets may still be useful tool, it would be best not call it faceted classification when facets are applicable to only some content types.

While faceted classification tends to be quite limited in the number of its facets, non-classification faceted taxonomies, whether based on subject types or separate controlled vocabularies, could result in a rather large number of facets.

Faceted taxonomies that would not be considered faceted classification include those where multiple facets are created for organizing and breaking down subjects or when multiple facets are created for reflecting multiple different controlled vocabularies. These faceted taxonomies stretch the meaning of “facet,” since the facets are not necessarily faces, dimensions, or aspects, but simply “types” suitable for filtering.

Facets for organizing subjects

In faceted classification we assign an object or content item to multiple different classes. However, for classification, these classes are relevant to the content item as a whole. This contrasts with indexing or tagging for subjects or names of relevance that occur within a text or are depicted within a media asset. These names and subjects can be grouped into facets for filtering/limiting search results, without being about the “classification” of the content item.  This is common for specialized subject areas. Faceted taxonomies provide a form of guided navigation and are easier to browse and use than deep hierarchical taxonomies, so a large “subject” taxonomy could be broken down into specific subject-type facets.

Examples of specific subject-type facets include:

  • Organization types
  • Product types
  • Technologies
  • Activities
  • Industries
  • Disciplines
  • Job roles
  • Event types
  • Topics

The “Topics” facet is then used for the leftover generic subject concepts that do not belong in any of the other specialized facets. Unlike faceted classification, each facet is applicable to only some content items.

Any content item could be tagged with any number of concepts from any number of these facets. The facets make it easier for user to find taxonomy concepts and combine them. But the facets are not for “classifying” the content.

While faceted taxonomies should also ideally be mutually exclusive, in contrast to the principle of faceted classification, the occasional exception of a concept belonging to more than one subject-type facet (question word of “What”) does not create a problem in search. For example, the same concept Data catalogs, could be in the facet Product Types and Technologies, as long as this type of polyhierarchy is kept to a minimum to avoid confusion. This would not be considered a case of classic polyhierarchy, because it’s not simply a matter of different broader concepts, but rather different facets or concept schemes. It is an attempt to address a different focus or approach to the topic that results it being in more than one facet, offering an additional starting point for searchers.

Facets for organizing controlled vocabularies

Faceted filters/refinement may be based on different controlled vocabulary types: one or more of term lists, name authorities, and subject thesauri/taxonomies. The “facets” are based on how the set of multiple controlled vocabularies is organized rather than based on “aspects” of the content.

Facets could be used for any controlled vocabulary filters that are logical, such as:

  • Named people (mentioned/discussed)
  • Organizations (mentioned/discussed)
  • Products/brands (mentioned/discussed)
  • Divisions, departments, units (mentioned/discussed)
  • Named works/document titles (mentioned/discussed)
  • Places (mentioned/discussed)
  • Topics (mentioned/discussed)

Because these facets reflect controlled vocabularies of concepts used to tag content for relevant occurrences of the subject/name and not for classification of the content, this kind of faceted taxonomy would not be considered faceted classification. There could, however, be additional faceted classification types, such as content type.

The Topics facet could contain a large hierarchical taxonomy or thesaurus. As such, this faceted search/browse structure, may not even be considered a “faceted taxonomy,” but rather merely a faceted search interface to a set of taxonomies. Thus, there is even a nuanced difference between a faceted browse UI that utilizes at taxonomy (among other controlled vocabularies), and a “faceted taxonomy.”

Facets for heterogeneous content

Finally, whether a faceted taxonomy is considered an implementation of faceted “classification” or not may depend on the context and type of content. If the content is homogeneous and all items share the same facets, then it may be considered faceted classification, but if the content is heterogeneous, and the facets are only relevant to some content, then it would not be considered classification.

Consider the following example of specialized subject-based facets for the field of medicine:

  • Diseases or conditions
  • Body parts (anatomy)
  • Sign and symptoms
  • Treatments
  • Patient population types

If all the content comprised just clinical case studies, then these facets actually could be considered faceted classification, since they all apply to nearly all the content and are aspects of the content. The content is classified by these facets. On the other hand, if the content dealt with all kinds of documents that had something to do with health or medicine, then these facets would not be for classification of the content but rather just for grouping of subjects for search filters.

When faceted classification is not a taxonomy

Attributes for computers
Finally, I would not consider all faceted structures to be faceted taxonomies.

Taxonomies are primarily for subjects and may include named entities. Content types/document types may also be included in the scope of taxonomy. There exists additional metadata that may be desired for filtering/refining searches that is out of scope of a definition of taxonomy. This includes date published/uploaded, file format, author/creator, document/approval status, etc. If it is important to the end users, these additional metadata properties could be included among the browsable facets and be considered classification aspects.

Attributes are a form of faceted classification, but a set of attributes is not really a faceted taxonomy. Often ecommerce taxonomies are presented as examples of faceted taxonomies. In fact, ecommerce taxonomies tend to be hierarchical, as they present categories and subcategories of types of products for the users to browse. At lower, more specific levels of the hierarchy, the user then has the additional option to narrow the results further by selecting values from various attributes that are shared among the products within the same product category. These include color, size/dimensions, price range, and product-specific features. I would not consider numeric values to be a taxonomy, but some attributes, such as for features, are more within the realm of taxonomies. Whether these should be called facets or attributes is a matter of debate. More about attributes is discussed in my past blog post “Attributes in Taxonomies.”


Not all faceted taxonomies are faceted classifications, but some are. Not all faceted classifications are taxonomies, but some are. The differences are nuanced, and end-users may not care nor need to know these naming distinctions, as long as the taxonomist should. Having a deep understanding of facets helps taxonomists and information architects design the facets better. The goal is to serve the users with the most suitable faceted design to serve their needs and accommodate the set of content.

Sunday, January 14, 2024

Learning to Create Taxonomies

Knowledge of what taxonomies are, what they are for, and how they are used is quite widespread, even if there are uncertainties and disagreements around the definition of “taxonomy.” People who often look up digital information are familiar with various presentations of taxonomies for selecting terms linked to content. These include hierarchical trees of topic and subtopics to browse, scroll boxes of controlled terms, type-ahead or search-suggest terms that appear below a search box after the first few letters are typed into the box, and terms or named entities grouped by various aspect types (facets) in the left margin to select from in order to limit/refine/filter search results.

Why Learn Taxonomy Creation

There is a big difference, however, between being able to use taxonomies and being able to create taxonomies.

While it is usually best to leave taxonomy creation to the experts, taxonomists are not always available, or the needed taxonomy may be small or apparently “simple,” so it may not be economical to hire a contract taxonomist or a consultant. In other situations, the taxonomy subject may be quite technical, and it would seem preferable to have subject matter experts, rather than an external taxonomist, create the taxonomy.  Thus, people who are not professional taxonomists often create taxonomies.

Generative AI now makes it easier for anyone to “generate” a taxonomy. However, the knowledge of taxonomy principles is needed to make necessary corrections and edit the taxonomy to achieve a decent level of quality. Generative AI should not be used to fully create a taxonomy (which could in fact be extracting published taxonomies violating their copyright), but rather it may be a used as a tool facilitate parts of the taxonomy creation process. (See my post “Taxonomies and ChatGPT.”) The technology thus makes it easier to create taxonomies for those who are not taxonomists and have limited time for taxonomy creation tasks.

There is also the matter of taxonomy maintenance. After a contract taxonomist or consultant creates a taxonomy and leaves, the taxonomy still needs to be kept up to date, with new concepts added and others changed, and over time expanded. While documentation and guidelines written by a taxonomy consultant are helpful, a good understanding of taxonomy creation principles is also needed by anyone responsible for expanding or maintaining a taxonomy.

Finally, taxonomy creation is a collaborative effort, involving stakeholders in various roles (project management, content management, digital asset management, information technology tagging, research, user experience, search, etc.) who are invited to contribute their perspectives. Stakeholders can provide better insights to a taxonomy if they have a better understanding of taxonomy principles. Taxonomy project managers in particular need to understand taxonomy creation even if they are not doing the actual taxonomy creation work.

How to Learn Taxonomy Creation

Fortunately, there are many resources to learn the principles and standards of taxonomy design and creation. There is, of course, my book, The Accidental Taxonomist, which, as the name implies, is intended for anyone who finds themselves, perhaps by “accident” in a position that requires them to create, edit, or manage taxonomies.

Heather Hedden delivering a taxonomy workshop
There are also various half-day and full-day workshops at conferences, virtual short courses through professional associations and other organizations, and asynchronous online training. These usually involve some exercises for practice and provide the appropriate amount of training for getting started with creating taxonomies. I’ve offered various kinds of training, both independently and through other organizations, over the years. My current course offerings are on my website

Upcoming Taxonomy Course

The next live (virtual) course I will offer is a new course called “Controlled Vocabularies and Taxonomies”  offered through HS Events, on GoToWebinar over four weekly sessions from February 29 though March 27. I will teach this course live (with ample time for Q&A) just once, after which it will become available as a recording for purchase.

HS (Henry Stewart) Events are best known for their dominance in the field of digital asset management (DAM), but the course I will teach is not limited to DAM professionals. Actually, this course is most appropriate for the expanding scope of HS Events, which will introduce a Semantic Data conference event, which includes the subject of taxonomies, co-located with its DAM conferences in London and New York in 2024.

The subject of taxonomies fits nicely into four sessions. The first session is an introduction to the definitions, types, uses, benefits, and standards for taxonomies. The second deals with project management side of planning and researching for creating controlled vocabularies and taxonomies. The third session gets into the details of creating terms and relationships. Finally, the fourth session takes up design and implementation issues.

This course is most similar to the course "Metadata and Taxonomies" which I had taught through the Rome, Italy-based training company Technology Transfer S.r.l from 2019 to 2023, and which I decided to discontinue offering. The scheduling is now better: Instead of two consecutive days of four hours/day it is spread out over four weekly shorter sessions with a dedicated encouraged Q&A time. Also, the sessions start two hours later than the Rome-based course (10:00 am instead of 8:00 am EST). I have also updated the content, which was getting a little stale after several years, and I added more new graphics. Finally, the registration fee is considerably lower than the Technology Transfer course. You can also take advantage of a 20% discount (code JANUARY20) if you register before January 31.

Sunday, December 31, 2023

IT and Taxonomies

Taxonomies are related to many fields of work, including knowledge management, information architecture, website design, website marketing at SEO, document management, terminology management, publishing, product management (for information products), content management and strategy, digital asset management, machine learning for classification, natural language processing for auto-tagging, data management, library and information management, and information technology. Information technology is relevant to the implementation of all taxonomies.

Why is IT involved in taxonomies?

Taxonomies link users to content (and taxonomies extended into ontologies also link users to data), but this linking relies on technology. The technology could be a kind of software, such as a content management system that supports the tagging and retrieval of content by taxonomies along with the feature of taxonomy management. Often, however, additional technology is needed to link multiple software systems together, with APIs, and to move data across systems, with extract-transform-load (ETL) tools. Taxonomies are increasingly built in the SKOS (Simple Knowledge Organization System) standard/data model, which enables taxonomies and other knowledge organization systems to be machine-readable and not just human readable.

Taxonomies are a concern of information technology professionals as they are the owners of, and often also the developers of, the systems in which taxonomies are implemented. The systems could be completely internally developed, or they could be licensed software that typically requires some customization or integration with other systems. In my experience as a taxonomy consultant, I have typically engaged in conversations with those in IT as key stakeholders of the taxonomy. However, the degree of the involvement of IT professionals in the taxonomy itself can vary.

In custom taxonomy implementations, such as in an information service/product or in an ecommerce business, IT professionals are usually not involved in the actual design of the taxonomy, but taxonomists or others who create that taxonomy need to collaborate with IT professionals to understand the system’s capabilities and limitations and may impose requirements. Taxonomists are concerned with how the taxonomy will be displayed to the users, how the users can interact with the taxonomy, how tagging is done, and how the search functions. Custom software development has great flexibility in how it supports a taxonomy.
In implementations of taxonomies in licensed software, there may still be some development work for the IT professionals, but there are limits to what can be done or changed.

Commercial content management systems (CMS) that allow for the custom development of the user interface, referred to as “headless” CMSs, however, are becoming more common. The user interface in particular is very significant to how a taxonomy is designed and how it functions.

Who in IT is involved in taxonomies?

Those who work in IT departments with involvement taxonomies could be in roles doing development or support for systems that manage and consume taxonomies, or they could be in systems integration roles. Additionally, there are taxonomy/metadata/ontology specialists who work within the IT department of an enterprise, especially if a knowledge/information management department does not exist in the organization.

In a survey of taxonomists I conducted in January 2022 for the 3rd edition of The Accidental Taxonomist book, of 162 people who do taxonomy work for their employers, which are not consultancies creating taxonomies for others, a multiple-choice question asked what area they work in. Information technology ranked 4th out of 11 choices, with 17% of the responses, following the areas of knowledge management, content management/strategy, and product development/management, yet ahead of the specialties of library, user experience, marketing, and others.

The survey also asked all respondents to provide their job titles, and some of those working in taxonomies have job title that are closely associated with information technology. These included titles of IT Data Analyst, Data and Technology Platform Products, SharePoint Product Owner, Senior Solutions Consultant, Implementation Project Manager, Data Architect, Senior Manager - Graph Solutions, Enterprise Architect, Staff Engineer - Systems, Information Governance Engineer, Head of Technical Services, and Director of Solutions Delivery.

What does IT do with taxonomies?

From my experience as a taxonomy consultant, I have observed that those working in IT, in their efforts to facilitate the adoption of new software and features that make use of taxonomies, may include starter taxonomies within the tool, whether selected from offerings of software vendor or created by the IT staff themselves. For example, IT professionals might create simple controlled vocabularies in the SharePoint term store, such as for document types, departments, locations, etc., so that users can start using the search refinements right away, and there is also an example of the functionality of taxonomy, which can be improved upon and expanded by someone else later.

Then there is enterprise taxonomy/ontology management software, which should be connected to search systems, content management systems, and tagging systems (if not using a tagging module of the taxonomy management system). In my experience working for a taxonomy software vendor, the IT department was often involved in the software purchasing process, if not actually leading the decision-making. Representatives from the IT department attend pre-sales demos of the tool, ask questions, and compile and compare system requirements when requesting a proposal.

That taxonomy is actually an area concern of IT, was also made clear when I saw that taxonomies were mentioned in a section within a chapter on knowledge management-related systems in my son’s introductory Management Information Systems textbook for a required course for his B.S. in Information Technology.

In sum, IT professionals who support enterprise knowledge or information management systems need to have a basic understanding of taxonomy principles, standards, benefits, and uses. My website contains various taxonomy resources. Some IT professionals may even want to go further and design and create small taxonomies (lacking the time to create large taxonomies), and they may want to read my book or attend my workshops or online courses.

Thursday, November 30, 2023

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 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.

Tuesday, October 31, 2023

Taxonomies for Learning and Training Content

Taxonomies are primarily for tagging digital content to make it more easily found when users search or browse on taxonomy concepts. Content can be of various kinds: articles and research reports, policies and procedures, technical documentation, product information, contracts and other legal documents, marketing content, etc. A growing area of digital content is instructional or training content, especially corporate training for employees.

The need for taxonomies for training content

When an organization offers its employees a large number of training courses, it can be difficult for employees to find desired training. Having the training content tagged with controlled terms from a taxonomy makes it easier to find.

The training content may come from different sources and thus may come with different, inconsistent metadata already applied to it. An organization may have generic training (such as on diversity and information security) produced by a corporate training company, industry-specific training (such as anti-money laundering for financial services and retail industries) produced by a different training company, and company-specific training which is internally produced. An organization may also subscribe to an offering of business skills and technical skills training offered by one ore more third party, such as LinkedIn Learning. It may be very difficult to search across all these different sources.

Furthermore, simply searching on words in training course titles might not be effective, if topics are broad or the course titles are vague. For example, a search on “communication” may yield far too many results to sort through. A search on “writing” might miss a training course with a title of “Bringing out Your Voice” or “Use Plain Language.” Tagged with the concept of “Writing,” these courses can then be found.

Faceted taxonomies for training content

Sample faceted taxonomy for
training content in PoolParty

For the complexities of training content, a single topical taxonomy is not enough. There could be ambiguity as to the skill level or between training topic and training format. For example, the topic of “Manager training” is not clear as to whether it is for new managers or all managers. The topic of “Presentation slides” is not clear as to whether it is training on how to create presentation slides or if presentation slides is the training format/medium. This is where a faceted taxonomy can help. Facets are different aspects of content which can be combined as search filters.

Training content is especially well suited for facets. Examples of possible facets for training content are: Content type, Level, Role, Skill, Training Program, and Topic.  An example of taxonomy terms in each facet are as follows:
•    Content type: Video training
•    Level: Intermediate
•    Role: Customer support
•    Skill: Written communication
•    Training program: Upskilling
•    Topic: Timeliness

It’s important to keep in mind that facets should be mutually exclusive, so the same concept, such as “Customer support,” cannot exist in both the Role and the Skill facets. Distinguishing a role and a skill can sometimes be difficult. It important to separate out Role, though, because then there is the possibility to recommend training courses based on one’s Role.

Taxonomy facets are based on metadata properties, but there likely exist many more metadata properties than needed for the end-user to filter train content searches. Additional, administrative metadata properties should not be implemented on the front-end for course searches. These might include Organizational unit, Original source, Region, Access Level, etc.

Skills taxonomy sources and challenges

Developing a skills taxonomy facet has its own challenges. First of all, there are multiple goals of skills taxonomies. Enabling employees or their managers to find appropriate training is just one goal. Other purposes may be to describe job openings to found by candidates with matching skills, to find an expert with a desired skill to ask question of or have work on a project, or to map roles and skills to identify gaps and improve human resources strategies and professional development programs.

There are also varied sources for skills taxonomies. Managers and subject matter experts would list certain skills, which might differ from a list of skills proposed by human resources staff. A taxonomist, metadata specialist, or information architect working on a taxonomy would come up with a slightly different list of skills, probably not as detailed. Finally, there are external sources, but these might not be appropriate to a specific organization. The largest, best known published taxonomy of skills is ESCO (European Skills, Competences, Qualifications, and Occupations), but with 13,890 skills, it is much too large and detailed for any one organization. It might be best to start with any skills list that the HR department has and build it out further with recommendations from managers, but not as detailed as some subject matter experts might suggest. External sources could be consulted to fill in some gaps.

There is the potential to get too detailed in creating a hierarchy of skills, and some of the narrower concepts may end up being specific topics and not exactly skills. For example, a skill of project management could get narrower concepts for different project management methodologies and then various components of each methodology.  This is would not be appropriate for a skills taxonomy, although, if important, these narrower concepts could be included in a Topics facet instead.

Presentations on taxonomies for corporate training content

My most recent conference presentation and my next conference presentation are both about taxonomies for corporate training content.  On October 16, I presented at the LavaCon content strategy conference in San Diego “Leveraging Semantics to Provide Targeted Training Content: A Case Study,” which was jointly presented with PoolParty software proof-of-concept project customer Esther Yoon of Google gTech. In addition to some of the issues described in this blog post, I also discussed how facets can be customized and how roles and skills can be linked for recommendation, and Esther presented how the POC improved the discovery of training content for those in roles related to customer support.

On November 6, at Taxonomy Boot Camp conference in Washington, DC, I will present “Challenges in Creating Taxonomies for Learning & Development,” which will be jointly presented with Amber Simpson of Walmart’s Walmart Academy, also a PoolParty software customer. In addition to issues described here, I will also provide specific examples of challenges in creation a Skills taxonomy facet. The slides will also be made available afterwards.