Taxonomy Definition

I usually explain that a taxonomy is a structured kind of controlled vocabulary, which is list of terms (or concepts) usually used to tag content to aid in its retrieval. The structure can be hierarchical, faceted, or a combination. Other people have defined taxonomies for a general audience in more simplistic ways as a kind of hierarchical classification system. So, while a taxonomy has two main features (naming and structure), my preferred definition has focused on the controlled vocabulary and naming aspect, whereas other definitions focus on the hierarchical classification aspect of taxonomies. However, a taxonomy and a classification system are not necessarily the same. While it is understandable that a definition is simplified for a general audience, it should not be simplified to the extent of being misleading.

I have blogged previously on the differences between taxonomies and classification systems, so I won’t repeat all the differences again.  The main point is that a classification system is generic and rigid and is intended to be used widely, such as the Dewey Decimal Classification for libraries, whereas a taxonomy tends to be customized for a particular use case and context and is flexible and undergoes changes.

Meanwhile, there are also a few well-known classification systems that are called “taxonomies,” such as the Linnaean taxonomy of organisms and Bloom’s taxonomy of educational objectives.  These seem quite different from the information-retrieval type of taxonomy. The Linnaean hierarchical levels have names (Kingdom, Phylum, Class, etc.). The relationship of the hierarchical levels to each other are not all of the thesaurus standards: generic-specific, generic-instance, or whole-part. Rather, the Linnaean taxonomic relationship are generic-specific only, or more precisely that of member of class or subclass. Bloom's taxonomy has a completely different hierarchical model that does not follow thesaurus standards at all.

How does a taxonomy of concepts for information retrieval relate to a scientific taxonomy? They are similar, and the differences are not so great that there should be considered different meanings of the word “taxonomy.” If we consider that taxonomies are systems to name and organize things hierarchically, then a taxonomy for information retrieval, comprised of terms for tagging and retrieving content (documents, images, etc.), can be considered a taxonomy of a controlled vocabulary, in contrast to taxonomies of things, such as organisms. This is a slightly different perspective than to consider a taxonomy as a kind of controlled vocabulary, as I previously had. The following diagram illustrates a possible way to consider how information-retrieval taxonomies related to classification systems and controlled vocabularies.

Several kinds of knowledge organization systems are defined by their published standards. For thesauri, there are ANSI/NISO Z39.19 and ISO 25964. For terminologies, there is ISO/TC 37/SC 3 and other related standards. For ontologies, there is OWL (Web Ontology Language) from the W3C. There is no standard, however, specifically for “taxonomies” or even for “classification systems,” which is a reason why these remain difficult to define. The designations “classification system,” “classification scheme,” and “taxonomy” have been used interchangeably.

Wikipedia provides the definition at the entry for Taxonomy: “A taxonomy (or taxonomical classification) is a scheme of classification, especially a hierarchical classification, in which things are organized into groups or types.” But then it goes on to say, “it may refer to a categorisation of things or concepts.” Thus, an information-retrieval taxonomy is a categorization of concepts (also called terms in a controlled vocabulary). It is not a classification system, since the goal is not to classify things, not even the things tagged with the taxonomy concepts, but rather to organize the set of concepts that have been identified as appropriate for tagging and retrieving a set of content.

Taxonomies to Bridge Silos

There is increasing interest in organizations to “break down silos” of content and data. Silos may be different software applications, distinct web or intranet content, or merely different computer drives and folders. The goal is to enable search and retrieval across content that is stored in different content/document management systems and shared folders and the analysis and comparison of data stored in different kinds of database management systems, records management systems, and spreadsheets. This results in better, more complete information to enable more informed decisions and knowledge discovery, along with improved user satisfaction, while also saving time. Breaking down or bridging such silos was a theme of my two most recent conferences.

 

LavaCon: Connecting Content Silos

The 20^th annual LavaCon conference on content strategy, held October 23-26 in New Orleans, had the theme this year of “Connecting content silos across the Enterprise.” The conference had a number of presentations tied to the theme, 10 of which had “silos” in their titles. Two presentations I especially enjoyed were by leading content strategy consultants about how to connect silos.

Sarah O’Keefe of Scriptorium, in her presentation “From Silo Busting to CaaStle Building,” with a fairy tale castle metaphor, explained that completely unified content cannot be achieved, because CMSs are tuned to specific content domains, corporate websites accommodate different goals of different groups, content silos have their own delivery pipelines, and silos often match the organizational structure. Her solution was to provide Content as a Service (CaaS), or a “CaaStle in the cloud(s).” Silos are kept, allowing for unique requirements, and perhaps reduced in number, but are connected were needed.

Val Swisher of Content Rules, in her presentation “Creating a Unified (Siloed) Content Experience: The Importance of Terminology and Taxonomy,” explained that siloed content results in different user experiences for each silo. But silos are not going away, because there is no single toolset, particular content has its owners, and certain content may be considered special. Therefore, the user experience should be improved to “ensure that all content looks like it comes from the same company” and to “eliminate the confusion that users experience when they consume content created by various silos.” This is done by standardizing the content, the search, page layout, navigation, content types, terminology, and taxonomy.

At LavaCon, I presented a pre-conference workshop with the title “Using Taxonomies and Tagging to Connect Content Across the Enterprise.” While most of my workshop addressed the general principles and best practice for taxonomy creation, along with the basics of tagging, I did discuss a how centrally managed taxonomy, external from but linked to various content management systems and other applications or repositories of content, can bridge silos. Taxonomy management software positioned as “middleware” such as PoolParty, connects to these different content applications and repositories, and then the taxonomy is presented to the user in a single user interface.

Taxonomy Boot Camp: Taxonomy Breaking Down Silos

At the annual Taxonomy Boot Camp conference, held November 7-8 in Washington, DC, and co-located with the KM World conference, I spoke in a two-presentation session titled “Taxonomy Breaking Down Silos.” The idea is that taxonomies provide the connections to break down barriers between different systems and teams. I presented on taxonomy linking jointly with Donna Popky, Senior Taxonomy & Information Architecture Specialist, Harvard Business School. I explained the principles of taxonomy project linking, and Donna presented a case study of taxonomy linking using a hub and spoke method to link separate taxonomies managed by different business units with separate content repositories for different purposes at Harvard Business School. So, this was a case of creating a hub taxonomy linked to the various business unit spoke taxonomies.

The other speaker in the session, Rachael Maddison, Content Infrastructure Architect & Taxonomy Product Manager for Adobe Digital Media Experience and Engagement, presented on taxonomy adoption across corporate silos and not merely content silos. Collaboration plays a role in wider taxonomy adoption, and as Rachael stated: “Mapping or merging can’t happen until there is stakeholder buy-in.”

Over the years, my list of the benefits of taxonomies has grown. Linking data, content, and corporate silos are additional benefits. This can be done with a single, enterprise taxonomy or with multiple linked taxonomies. In either case, the taxonomy needs to be managed externally from any individual siloed application in a dedicated taxonomy management system. Taxonomies can then break down corporate silos and connect content and data silos.

The Accidental Taxonomist, Third Edition

The third edition of my book, The Accidental Taxonomist, will officially be published November 7, and I just received  advance printed copies, so now is a good time to talk about. Details of the book are on its website. For those who wonder how this edition differs from the prior edition, I discuss that in the preface of the 3rd edition, which I have copied here.

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I am thrilled that taxonomies are as relevant now as they were when I was writing my first edition in 2009 and second edition in 2015 and even more so. Some people had previously thought that improved search algorithms would largely replace the need for taxonomies, but users want to be able to select search refinement terms, and the greater adoption of search has led to more taxonomies. Some thought that AI technologies of text analytics and auto-classification might replace human-created taxonomies, but, on the contrary, they made taxonomies more valuable. Some thought that ontologies would replace taxonomies, but instead ontologies have connected and extended taxonomies, providing additional uses for taxonomies. Innovations and trends in digital content and data have given rise to new uses for taxonomies, including support for recommendation, personalization, data-centric enterprise knowledge management, voice of the customer analysis, and chatbot design.

There are signs of interest in taxonomies in various places: social media posts, conference presentations and workshops in a greater number of different conferences, and a continued strong enrollment trends in my online taxonomy course. Taxonomy consultants I know are doing well with business. A search on “taxonomy” in Google Trends shows a continued steady interest in the term since around 2006. Members of the Taxonomy and Ontology Community of Practice LinkedIn group has grown from 3,330 in 2015 to 5,564 in June 2022. More people continually get involved in taxonomy work, as our survey of taxonomists indicates relatively more people with fewer years of experience. (See Appendix A, Question 2.) The number of jobs for taxonomists continues to increase, as evidenced by repeated taxonomy job searches over the years on job boards, job alert postings, and direct queries colleagues of mine have reported receiving from recruiters. The trend toward remote work, especially for knowledge workers, has opened up more job possibilities for taxonomists, who are no longer limited by their geographic location, which had previously been an issue for this very niche specialization. We may soon see more digital nomad taxonomists living and working all over the world.

Meanwhile, as I have continued to engage in taxonomist discourse, consulted for more taxonomy clients, and attended and created new conference presentations, I have continued to learn more and thus refine how I understand and explain taxonomies. It is time that this book also catches up to how I have been explaining taxonomies in my most recent presentations and workshops. I have even revised my thinking on the definitions and types of controlled vocabularies, so the definitions and types section of chapter 1 has been rewritten in this edition. Also in the first chapter, additional uses for taxonomies have been included.

In addition, perspectives on taxonomies have gradually changed, and I am finally catching up. One of the main updates to this third edition has been to move decisively from the traditional thesaurus model and adoption of the language of the SKOS (Simple Knowledge Organization System) with respect to taxonomies. Most significantly this means referring to concepts and their labels and not to terms. An oft repeated phrase is that it’s about “things, not strings.” Concepts are things, whereas terms, as words or phrases, are merely strings (of text). This has also involved removing the equivalence relationship section from the chapter on relationships and adding a section on alternative labels to the chapter Creating Concepts and Labels (which has been renamed from Creating Terms).

When I updated the 2nd edition, I was working at the time for a library database vendor, so my perspective was somewhat biased toward that industry and use case, despite having had experience has a consultant too. Now, with not only more consulting experience in the interim, but from the perspective of working for a taxonomy software vendor, I see better the varied uses and implementations of taxonomies. As a result, I have changed number of the examples. I also made updates to the chapter on manual tagging (formerly called human indexing) and replaced many references to “indexing” with “tagging,” in recognition of the more commonly used term, although they are not identical. I had entered this field as an indexer, but I should no longer let my indexing roots influence my perspective. I also cut out some information on thesauri, such as details of the various thesaurus print display formats.

This edition features a new chapter on ontologies. This is not merely because ontologies may be of interest to taxonomists, but because ontologies in business and industry are increasingly created as an extension of existing taxonomies thus enabling taxonomies to serve more purposes. A convergence of taxonomies and ontologies is now possible with SKOS-based taxonomies, whereby both taxonomies and ontologies are based on RDF and other W3C standards. I am also seeing more taxonomist/ontologist hybrid jobs posted.

Technologies and vendors change, so the chapters on software and auto-categorization needed updating. There have been evolving trends in software, such as the ability to connect and integrate with other systems through APIs, instead of exporting and importing taxonomies, and including auto-tagging within the same tool. Other updates include data from a new survey, nearly all new screenshots, and updated information on taxonomy courses, conferences, and other resources in the final chapter. About half of the chapter head quotes are also new.

In case you missed it in the preface to the second edition, the updates from the first to the second edition (and thus also updates between the first and the third edition) include the following: managing taxonomies in SharePoint, the relationship between taxonomies and metadata, reference to updated ISO standards of 25964 of 2011 and 2013, the introduction of the SKOS standard, and improved explanations on planning and designing taxonomies, along with results of a new taxonomist survey and software information updates.

Taxonomies and Semantics

How are taxonomies related to “semantics”? I considered this question, as the latest conference I participated was SEMANTiCS, the European conference of semantic technologies, which took place this year in Vienna, Austria, September 13 - 15. Topics presented and discussed in this conference included ontologies, knowledge graphs, semantic models and reasoning, linked open data, machine learning, natural language processing, and other language technologies. Yet taxonomies were also discussed in a number of presentations. In contrast to a conference dedicated to taxonomies, such as Taxonomy Boot Camp, where taxonomies are the focus, at SEMANTiCS, in the context of semantic technologies, taxonomies are a component or an underlying layer in the application of semantic technologies.

Semantics means “meaning.” Like the words “taxonomy” and “ontology,” there is a traditional meaning that is more academic and, in the case of semantics and ontology, also connected to philosophy, but there is also a modern meaning that deals with information science and knowledge management. For example, “semantic search,” means searching for concepts and ideas, not merely matching search strings of text. Thus, a taxonomy or thesaurus supports semantic search by comprising unambiguous concepts of “things, not strings” of text. 

Semantics also implies Semantic Web, with technology that complies with the Semantic Web that have been developed by the World Wide Web Consortium (W3C). The Semantic Web, also known as Web 3.0, is not component of the World Wide Web nor a different web, but rather a kind of extension of the web to include not merely content and simple hyperlinks, but also all kinds of data that is semantically linked (where the links/relationships also have meaning). The Semantic Web allows more complex data, and data stored and organized in graph databases, to be machine-readable. This could be either on the public web or within an organization that follows Semantic Web standards for managing its data and content. 

Taxonomies were mentioned in a number of other presentations as a given foundation to ontologies, semantic networks, or knowledge graphs. For example, taxonomies and ontologies were the basis of knowledge-based recommendation system, described by Andreas Blumauer in his presentation on that subject. In her talk “ Real World Case Studies: Five Success Factors to Implementing an Enterprise Data Fabric,” Lulit Tesfaye explained that the components of a data fabric are metadata, taxonomy, ontology, knowledge graph, connections and integrations, and front-end applications.

A session titled Taxonomies included a talk on “Taxonomy and Terminology,” compared and contrasted taxonomies and terminologies with respect to their kinds of terms and purposes, but also explained the semantics role of taxonomies. The presenter, Klaus Fleischmann, said that terminologies guide content creators, ensuring consistent, correct use of language company-wide, whereas taxonomies provide a semantic layer on top of content and metadata, often for semantic applications. Fleischmann also explained that taxonomies can be extended to ontologies or, in his words, taxonomies “modeled relationships via ontologies.”Also speaking in the Taxonomies session, Nimit Mehta whose presentation was titled “The Semantic Data Stack - A user story on building a data fabric,” Mehta described taxonomies as “A layer between your data and your business applications” and a “governance layer.”

Finally, I presented a taxonomy-related tutorial, although not on taxonomy creation alone, but rather titled “Knowledge Engineering of Taxonomies, Thesauri, and Ontologies,” in which I explained that taxonomies and ontologies are not so much distinct knowledge organization systems, but rather than ontologies are a semantic layer that are applied to and extend a taxonomy, giving it a greater degree of semantics. 

I hope to participate in the next SEMANTiCS conference in September 2023 in Leipzig, Germany.

SKOS-XL for Taxonomies

I recently posted about SKOS (Simple Knowledge Organization System). If you have read anything about SKOS, then you might have come across SKOS-XL (SKOS eXtension for Labels) and wondered what that is. The World Wide Web Consortium (W3C) released its recommendations for SKOS and SKOS-XL at the same time in 2009 but chose to make them separate recommendations. One way to see it is that, by separating out SKOS-XL, SKOS is indeed truly “simple.” In the detailed SKOS reference, SKOS-XL is an appendix. 

www.w3.org/TR/skos-reference/skos-xl.html

 

Extending labels to become resources

“Things, not strings” is a tagline for semantic models, such as SKOS, which emphasize concepts in taxonomies and other knowledge organization systems and not terms or words. Of course, strings of text exist, and when associated with concepts they are called “labels.”  The distinction between a label and the concept that the label describes may seem indistinguishable or perhaps just philosophical. The main difference is that concepts are unique within a taxonomy, but labels are not. A concept may have multiple labels (synonyms or names in different languages), and the same label might apply to different concepts (homographs).

SKOS specifies preferred labels, alternative labels, and hidden labels as options for concepts. Hidden labels can be considered as a type of alternative label that should never be displayed. Alternative labels may display, depending on the front-end application. Preferred labels are what are displayed, especially in hierarchies and facets. 

Concepts, as things, have properties or characteristics. Labels do not. But sometimes there are reasons to assign properties to labels, such as to indicate the purpose or use of different labels. In this sense, you would want to turn a string into a thing. More correctly, a thing is called a resource, as described by the Resource Description Framework (RDF) the model upon which SKOS is based. This is what SKOS-XL supports: converting labels to resources. It does this by adding three more elements not found in SKOS: label, label relation, and literal form. It is the label relation in particular that enables the extension to establish a link between a concept and a label. Further details are in the W3C's SKOS-XL recommendation, which I am not going to repeat here.

Use for SKOS-XL

A typical use case for SKOS-XL to assign properties to labels is if you want to have different labels for different user groups, such as a medical taxonomy for shared medical content to be accessed by both medical professionals and lay people. Medical professionals may prefer a concept labeled Neoplasms, while lay people could call it Cancer. Different user groups could be based in different regions. Although different ISO-code based language labels can be used to distinguish regions in addition to language (such as en-US and en-GB), you may not want to duplicate the vast majority of preferred labels and merely distinguish the few that are actually different.

While SKOS permits multiple alternative labels, aside from hidden labels, there is no way to distinguish their types or purposes in SKOS. You may want to alternative labels support search in one front-end application and not another. You may want to designate official acronyms as distinct from other alternative labels. You may even want to distinguish between different kinds of hidden labels, such as those that should be hidden because they might be pejorative or offensive, and those that you wish to hide only from a type-ahead display because they are near duplicates of other alternative labels and too many alternative labels would clutter up the display. Finally, there may be alternative labels used by only certain users or in certain regions.

SKOS-XL lets you assign properties or attributes to labels. Assigning the purpose or use of the label is only one possibility, although it is the most common use of SKOS-XL. You may wish to manage more administrative metadata about labels, such as the source or origin of different labels. 

Implementing SKOS-XL

The principle of SKOS-XL is not complex, but implementation can be more challenging, and if you are building taxonomies with the SKOS-XL capability, you would want to use taxonomy management software that supports SKOS-XL, such as PoolParty. Taxonomy management software products are quite consistent when it comes to their user interface for supporting the editing of basic SKOS taxonomies, but they are not the same for creating and editing SKOS-XL labels, which is a less common function. 

Having properties, such as types, for terms is not new, but required some more innovation in the SKOS model of things (concepts), not strings (terms). It was common for non-SKOS taxonomy/thesaurus management software, which treated different terms with the same meaning as equivalence relationships, to support the customization of relationships, including the equivalence relationship. SKOS-XL ensures that this earlier feature is supported in the current standard, in machine-readable format.

For SKOS-XL to be more widely used and maybe even more elegantly supported requires a great sharing of use cases. I hope the taxonomist community will share their experiences with SKOS-XL, so we can talk about practices and recommendations and not just theory.

Further information:

• “Taxonomy Management Based on SKOS-XL” 2016 presentation slides

• “From SKOS over SKOS-XL to Custom Ontologies” 2016 webinar video and slides

•  “What SKOS-XL adds to SKOS” 2011 blog post by Bob DuCharme