Related Terms in Taxonomies and Thesauri

One of the benefits of a taxonomy is that there are relationships between the terms to support navigating to find the most suitable term. This could be the multi-level hierarchical browsing from broader terms down to more specific narrower terms. In a strict definition of a taxonomy, the only required type of relationship between terms is hierarchical. But sometimes it may be desirable to have relationships other than hierarchical.

ISO and ANSI/NISO standards for controlled vocabularies are very explicit on the criteria for hierarchical relationships between terms. The narrower term must be a specific type of, an instance of, or an integral part of its broader term, and it must be so in all circumstances, not just sometimes. If a pair terms seem as if they should be related, but their relationship does not meet the criteria for a hierarchical relationship, then they could have an associative relationship instead, which is also known as “related term” or abbreviated as RT. Examples include Schools as related to School busses, Computers related to Operating systems, and Business development related to Marketing

 

Those familiar with controlled vocabularies, know that the RT relationship is a standard feature of a thesaurus, whereas it is not common in taxonomies. However, the distinction between a taxonomy and a thesaurus can be blurred, and the kind of controlled vocabulary that is designed and implemented can have features of both, serving the needs of the users and the nature of the content and indexing. Therefore, taxonomists should have a good understanding of the associative relationship, in case the need arises and the system supports it.

The creation of an RT relationship is more subjective than the creation of hierarchical relationships. Even the standard ANSI/NISO Z39.19-2005 (r2010), in section 8.4 Associative Relationships admits "The associative relationship is the most difficult to define." The standard states that the relationship is created "on the grounds that it may suggest additional terms for use in indexing or retrieval." The fact that the relationship may be created but is not required in many circumstances leaves it up to the best judgment of the taxonomist.

The only required circumstance for the RT relationship (in a thesaurus or taxonomy that has associative relationships) is between two terms that share the same broader term and they have some overlapping meaning. Examples include Boats and Ships (sharing the broader term Vessels), or Tablets and eReaders (sharing the broader term Mobile devices), or Coats and Jackets (sharing the broader term Outerwear). But there would not be a related-term relationship between Coats and Mittens, even though they are both narrower terms of Outerwear, because they don’t have overlapping meaning.

More often, however, the RT relationship is created between terms with different broader terms or even from different hierarchies entirely. The ANSI/NISO Z39.19 standard provides guidance on this only to the extent of when the relationship may be created. Whether creating the relationship is a good idea or not is up to the taxonomist’s discretion and ultimately depends on what the taxonomist thinks would be helpful to most users.

Following are examples of possible RT relationships between pairs of terms:

Baseball RT Baseball players

Ventilation RT Fans

Bacterial infections RT Antibiotics

Appliance repair RT Appliances

Plastics RT Elasticity

Timber RT Wood products

Psychology RT Psychologists

Literature RT Books

There are different users of a taxonomy/thesaurus with different goals. Depending on what they are looking for, some users would welcome the guidance of a certain RT relationship, pointing them to a related term they had not considered but find helpful (such as E-commerce RT Online shopping); whereas other users are not interested in the related term and may find extra RT relationships getting in their way. The goal is to provide RT relationships that are probably relevant and helpful to the majority of users or “the average user,” while realistically not expecting to serve all users.

The following are examples of RT relationships that are not likely to be helpful to most users and thus better not be created:

Newspapers RT Advertising

Germany RT World War II

Athletics RT Doping

A good way to become more familiar with best practices for creating RT relationships is to browse published thesauri with these relationships. Thesauri that are available publicly to browse on the web include the ERIC (Educational Resources Information Center) Thesaurus, MeSH (Medical Subject Headings), UNESCO thesaurus, and the NASA thesaurus. These and others can be found on the American Society for Indexing’s website page OnlineThesauri and Authority Files. Note that related terms might be indicated as RT, Related concepts, or See also links.

Creating associative relationships is part of the creative work of taxonomists, but taxonomists must remember that serving the user is the primary goal.

Subject Searching: Why a Taxonomy, Thesaurus, or Controlled Vocabulary Still Helps in the Age of Search

Subjects, topics, index terms, keywords, controlled vocabulary, thesaurus, taxonomy. These all refer to an organized, precise way to find and retrieve desired information, where that information has been indexed to terms. Indexing content with subject terms can be manual or automated, but in either case the focus is on what the content is about, not what words appear in the text. The subject terms represent unambiguous concepts, which may have synonyms, but synonyms are often included as cross-references to redirect to the preferred term name and thus to the same set of content. Before the era of digital content, subject categories or index terms were the only method to find specific information, such as in a back-of-the-book index or business categories in the yellow pages.

Using subject terms to find desired content contrasts with using a search engine for full text search. Search is based on the occurrence of words, not concepts, so appropriate results can be missed if they use different wording for the same concept, and inappropriate results can be retrieved if a word has multiple meanings. The accuracy of search, without the additional support index terms/subjects, is dependent instead on the sophistication of algorithms. The combinations of algorithms have improved only slightly in the past decade or two. What has made a bigger difference in retrieving good results through search (without subject indexing), is that in many cases the volume of content has grown, and when search results are arranged by relevancy, a larger number of initially displayed search results are satisfactory.

There are two issues with this kind of search. Ordering results by relevancy is not always the preferred option. Sometimes searchers are interested in timely stories, so they want their results to be ordered by date, newest first, but when relying on a search engine, newer results might not all be insufficiently relevant.  Secondly, such results are good for the searcher who only wants to get some or enough information on a topic. If instead, the searcher wants to perform an exhaustive search and retrieve everything available on a topic, there will likely be relevant content that is missed in the search retrieval because it was worded differently. Indexing with subject terms improves both precision (accuracy, where incorrect content is not retrieved) and recall (comprehensiveness, where appropriate content is not missed).

The role that index terms play in the search process has evolved. Originally, researchers started with browsing a full list of subjects that may have been arranged alphabetically (as a traditional book-style index) or hierarchically (as a taxonomy), and they navigated the index to find more specific subdivisions as aspects of the main heading, or they navigated the taxonomy to drill down to the most specific term. As the volume of indexed documents or other content items has grown over the years, browsing and selecting a term from a taxonomy or thesaurus is often no longer as practical or sufficient. An individual term may have too many records indexed to it. Furthermore, many taxonomies and thesauri have grown too large to easily browse.

So, instead of taxonomy terms being used as the primary starting point to find desired content, taxonomy terms are more often being used to narrow or filter search results.  The user executes a search in the search engine, and if they get too many results, they can limit or filter the results by various aspects listed in the margin, including by indexed subject. (Other aspects could be date, document type, author, source etc.) The subjects can display in order of frequency of occurrence on the records in the search result set, and the user can select among them, rather than having to browse the entire taxonomy or thesaurus.

Use of subjects and other attributes to limit search results is becoming very common across various implementations, so most people are familiar with using them, such as enterprise search systems to find internal corporate documents, ecommerce websites for selecting products, library databases for selecting research articles.

The use of subjects to limit search results is similar to a faceted taxonomy, although the designation “faceted taxonomy” typically refers to a taxonomy where different types of terms are grouped into multiple facets. In other words, a faceted taxonomy involves several facets or filters, whereas a traditional taxonomy or thesaurus may comprise a single facet or filter, which may be used in combination with other, non-taxonomy filters.

I will be exploring and demonstrating this topic, specifically in the case of library subscription databases, in a presentation “Customer Focused Thesauri,” in addition to a pre-conference workshop on taxonomy creation, at the Computers in Libraries conference in Arlington, VA, in April.

Taxonomies for Filtering and Sorting

Taxonomies are versatile and may be used for various purposes. Originally designed to support hierarchical browsing of topics linked to content, they also may be implemented to support more accuracy in searching. Most discussions of taxonomies have focused on browse and/or search, but taxonomies may function in additional ways: enhancing filtering and sorting.

Filtering

Taxonomies structured into facets serve a combination of search and browse, and thus serve what is often called “faceted search” or “faceted browse” (as described in a previous blog post, Faceted Search vs. Faceted Browse). However, it’s simpler, more accurate, and more helpful in understanding facets to consider a faceted taxonomy as serving a distinct role from browsing or searching, that of filtering.

Filtering is a common function which is not limited to use with taxonomies. Filtering can be done by non-taxonomy attributes, such as keyword, author, date, etc., if these are set up as metadata and implemented as filters. We see filters in situations which lack taxonomies in options in the email inbox or lists of documents in file management user interfaces. We also find filters on documents in SharePoint libraries and other content/document management systems, which may include taxonomies. In a user interface, the icon for filtering is a funnel (where you can imagine that it is lined with a cone-shaped filter paper).

Filters may be known by other names, such as “Refinements”/“Refine by” or “Limit by.” These designations may be used interchangeably, although they tend to be used different circumstances. “Filtering” may be done on search results or on a complete set of records, such as a list in a spreadsheet or table. “Refining” or “limiting,” on the other hand, would usually be performed only on the results of an executed search, as a further refinement or limiting factor on an initial set of search results which turned out too large. “Refining,” furthermore, suggests a more careful search process, so this name is more often used in research databases or other repositories of articles and resources.

A relatively small faceted taxonomy comprising short lists of terms for each facet/filter, from which the user can select from a displayed list or drop-down, is both easy to use and, with proper tagging, can achieve accurate retrieval results.

 

Sorting

Sorting is done on content in a spreadsheet or table, where data on content items is in different columns, with sorting done by an attribute of an individually selected column. Sorting could be by numeric order, by alphabetic order, by date, or by the mere presence of a binary value.  Indeed, most sorting does not involve taxonomies, but it can.  If a column is for “Topic” and items have been tagged with taxonomy terms, then the items can be sorted by taxonomy term topic.

The function of sorting with taxonomy terms may not be quite as common as filtering, since it is not done on search results but only on data in a table or spreadsheet. However, in many situations content items are presented this way: content in spreadsheets and databases, messages in an email inbox, content items in SharePoint libraries and various kinds of content management systems, and many other applications. Furthermore, it’s often simpler to sort than to filter.

Sorting is nevertheless a function associated with taxonomies, at least in the definition of taxonomy in the ISO standard for controlled vocabularies. ISO 25964-2:2013 in section 3.83 defines taxonomy as a “scheme of categories (3.5) and subcategories that can be used to sort and otherwise organize items of knowledge or information.”

The following screenshot from SharePoint shows the availability of both sorting (by clicking on the down-arrow next to a column name, such as Topic), and filtering (by selecting Topics in the filter pane on the right).

Summary

As taxonomies are versatile, the same taxonomy can be used for multiple purposes: browsing, searching, filtering, and sorting. However, a taxonomy design usually can be optimized for the user experience of only one or two implementations. So, a taxonomy that delivers a great user experience for hierarchical browsing, might not be best suited for filtering or sorting, or vice versa. Filtering is more accommodating for various taxonomies than is sorting, since it may not be necessary to display the entire taxonomy in filters.

Best Practices for Different Taxonomies

A question was recently posted to a group: “I'm wondering if anyone knows of a standard for designing taxonomies for industrial components (widgets).” So far, no one has replied.

To clarify, taxonomies for different subject areas and different content don’t have different standards. Standards, whether for interoperability, such as SKOS, or for structural design, such as ANSI/NISO Z39.19 or ISO 25964, are the same and just as relevant for taxonomies and thesauri in all subject areas. Taxonomies for different subject areas and content may have different design best practices, though. The published standards don’t spell out everything; there is room for design and style differences for different taxonomies, including those that differ in their subject domain and content.

Areas of taxonomy design best practices that may differ include:

• Degree of term specificity or granularity
• Depth of hierarchical levels
• Number of terms at the same level (i.e. the number of narrower terms a term has)
• Length of terms
• Use of parenthetical modifiers and other term label fields
• Additional attribute details for terms (notes or controlled value fields)

There are also issues of relationships between terms (whether a term may have more than one broader term, and whether there should be associative/related-term relationships) and how extensive alternative labels/synonyms shall be. Best practices for these issues, however, depend more upon the implementation and user interface for the taxonomy than on the subject area of the taxonomy.

In the case of an industrial component taxonomy, best practices for the aforementioned points would likely be of the following:

• There should be relatively high level of specificity of terms to include all components
• Depth of hierarchy that accurately reflects standard component categories and subcategories. So this could be deeper than for other, business taxonomies. Also, the levels of depth may vary in different parts of the taxonomy.
• The number of terms at the same level should also accurately reflect standard component categories and subcategories, so there could be a large number of terms at the same level.
• The length of the term should be complete and unambiguous, but any component number should be managed in a separate field.
• It may be desired to use some additional numeric or alphanumeric classification system. If so, the classification code would be another field or component of the term, separate from the term name, for purpose of sorting.
• Additional attribute details for each term would be desired and expected. These may include a component number, size, price, and other specifications. (Attribute fields may or may not be searchable. They are not for filtering, though, as facets are.)

In contrast, a consumer products ecommerce taxonomy would follow different best practices:

• Terms should not be too specific, not more specific than what users would be familiar with. Specificity should reflect the number of units (SKUs) covered by the term category. A term that refers to only 1-5 products is probably too specific. If there are additional refinement filters, then a category term may be broad enough to include 10-50 items.
• Hierarchy should not be too deep, probably no more than 3 levels.
• Terms per level should be limited, such as 3-12 terms per hierarchy level
• Term names should be concise, for easy browsing, yet unambiguous, usually 1-3 words
• Terms should probably not have any other fields/components or parenthetical qualifiers
• Attribute details would include at minimum product number/SKU and description. Price would be managed as a separate filter, rather than as merely an attribute.

These best practices are not “standards” because they tend not to be shared outside of an organization. Each organization comes up with their own policies and guidelines, just as they have their own taxonomies. The best practices could be considered internal standards, though. Regardless of what they are called, these guidelines should be documented and overseen as part of a taxonomy governance plan.

Engaging Others in Taxonomy Building

Whether you are building a new taxonomy from scratch or redesigning one based on an existing taxonomy, it’s important to engage other people in the process. There are two primary reasons:

1. getting input from those who will use the taxonomy, so that it will better suit their needs
2. getting buy-in and support from various stakeholders and users, so that it will continually be used, maintained, and funded

Even if you are the only person given the responsibility for creating and editing the taxonomy, and it’s no one else’s job to be involved, you should still seek the input of others.

It’s just common sense to get input from those who will use the taxonomy, although being respectful of their time in the process. If the taxonomy project can be stretched out over time, this means that asking for input does not happen too frequently.

Getting buy-in and support is an issue that is especially particular with taxonomies. People don’t always understand or respect the full benefits of a taxonomy. They may think that search alone or automatically generated keywords may suffice. Or they may prefer the simplicity of a search box. People responsible for tagging might prefer to avoid that task to save time and thus not adequately or correctly apply the taxonomy. Getting such people involved in the taxonomy creation process both educates them about the benefits of the taxonomy and gives them a sense of ownership by contributing to the process that is meant to serve them.

Others in your organization will use the taxonomy. If it’s for tagging and retrieving content internally, some people will use the taxonomy to tag content, and other people will use the taxonomy to help find content, but representatives from both groups of people should be invited to give input. If the taxonomy is to be use for public-facing content, those who will be tagging the content are still within your organization, and they should be consulted in the taxonomy design process.

Taxonomy consultants might lead 2-day taxonomy brainstorming workshops of stakeholders, conduct a series of interviews with stakeholders and users, and develop detailed use cases. While there is usually not the interest in demanding so much time of others when the taxonomy project has been assigned to a person on staff, the taxonomist should still engage other employees, at least at a minimal level. To get initial input, instead of in-person interviews, this could involve emailing a short list of questions to key users and stakeholders and the following up with a phone conversation to go over the answers. Draft versions of a starter taxonomy, which can be developed by means other than a brainstorming workshop, should be shared with stakeholders and subject-matter experts for feedback.

A rather technical taxonomy could start with asking the subject matter experts to submit lists of terms which the taxonomist would review with them and edit into proper taxonomy. In most cases, however, the taxonomist develops the draft starter taxonomy and then seeks feedback.

If you are not asking stakeholders to provide the starter taxonomy, where do you start? Someone asked me that question recently: “Where do I go to get my starter/basic/beginner list before I actually sit with staff?” I advised that you don't need much prior to talking with people. My recommendation, in the case of an enterprise taxonomy, would be to consider the following sources for draft taxonomy terms:

1. Folder names from shared drives
2. A prior attempt at creating a starter taxonomy, even if never implemented
3. Categories or tags set up in a content management system, whether or not fully used
4. Intranet/website site map or navigation menu labels
5. Product catalog top categories (as a starting point for a "Product" facet/Term set)
6. Org chart (as a starting point for a "Department" facet/Term set)
7. High-frequency use terms from search log reports

Include at least the two of these sources to share with people in meetings. I also recommend not editing them prior to initial meetings with people, but rather to get their input on this raw data.

Taxonomy work, is thus people-oriented work, like information architecture, in contrast to related fields of indexing and cataloging.