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What Are Taxonomies and How Should You Use Them?

Expert.ai Team - 30 October 2023

taxonomy definition

Table of Contents

  • What is Taxonomy?
  • Taxonomy in the Digital Age
  • Why Use Taxonomies
  • Why Taxonomies Need Natural Language Technology
  • Which Taxonomy is Right for You?
  • Taxonomy Management: Next Steps

Feeling overwhelmed by content management? Taxonomy can help.

But what is taxonomy?

Data is among an organization’s most valuable assets. Yet this information is different from one organization to another. And while each organization has its own nomenclatures, terminology and domains that are specific to its business, all organizations share a common need: to be able to access the information they possess and use it to the organization’s advantage. 

Machines, which we use to create, collect and share this data—in the form of documents, emails, video and audio files, CRM data and more—cannot inherently provide this information for us in a unified way. However, this is exactly what users need: to be able to find the information when they need it, from any source, quickly and effectively. Enter the taxonomy. 

What is Taxonomy?

A taxonomy provides a formal hierarchical structure for data within an organization or domain so that it can be easily retrieved and analyzed. 

As early as 300 BCE, humans were using a form of taxonomy to classify plants in Ancient Greece, and today, organizations are using taxonomies to help solve their enterprise data discovery problems and ensure that users are able to easily find the precise information they need. 

In the digital world, taxonomy applies structure to content and the relationships between them. It’s a system of content management that groups information based on terms stored as metadata. Taxonomies organize content into logical associations that are relevant for your business and the domains of knowledge that matter for you.

Taxonomy in the Digital Age

It’s no surprise that taxonomy has stood the test of time. Today’s organizations face the daily challenge of managing ever-growing quantities of text-based documents, web pages, news articles, social media, and other language-based assets. With an estimated 328.77 million terabytes of data being created every day, data discovery remains an ongoing challenge for managing enterprise information. 

When combined with AI and natural language processing (NLP), taxonomies make it easier for machines, and ultimately their users, to find any asset in the form of language. 

Why Use Taxonomies?

So, why use a taxonomy? Think about it this way: what value does any piece of information have if it’s not able to be discovered by users? If you cannot easily locate a document or report in your information archives, it may as well not exist at all. That’s why we say that data discovery is a high stakes activity. 

Taxonomies help you find the information you need, whether it’s on your website, your company intranet or any digital archive. When we’re searching for information, ambiguity can be the difference between a very targeted list of search results or hundreds of pages. 

The hierarchical structure of a taxonomy provides a system of organization that makes logical connections between data points. When it’s underpinned with a natural language understanding platform, a taxonomy is able to deal with the ambiguity in language and understand the precise context of a piece of information. For example, we can distinguish between content talking about the “gas pedal” or “gaslighting” from “gas” related to fuel. 

These differences in meaning are especially important when we are talking about a specific industry—insurance, life sciences or even a company’s own domain—that has its own language and jargon that do not make sense or have the same meaning in another context. 

These are just some of the reasons that enterprises use taxonomies:

  • For grouping, categorizing and organizing content
  • For making content searchable and retrievable
  • Finding correlations between content
  • Improving the user experience by ensuring they are able to find exactly what they need
  • Reducing the amount of time spent managing content
  • Tracking and managing content lifecycles

Why Taxonomies Need Natural Language Technology

We know that a taxonomy must be able to organize information into a hierarchical structure. To achieve this, machines must be able to understand the topics and ideas present in content, no matter how they are expressed. This is why taxonomies need natural language AI technologies.

Technology that relies on keywords or pattern matching doesn’t understand language. Instead, natural language understanding (NLU) technology understands meaning and interprets the concepts contained in text as a human does. Here, NLU adds depth and dimension to the content driven not by word frequency, but by relationship and meaning.

The NLU  technology that the expert.ai platform is based on enables deep analysis, including classification, of large structured and unstructured datasets. It identifies and associates content to the relevant categories and classes, generating available data for more effective search and analysis.

Applied for taxonomy development, NLU makes enterprise content and other external strategic information more accessible for business processes.

Which Taxonomy is Right for You?

There is more than one way to create a taxonomy. Depending on your industry, you can use one of the many industry-specific taxonomies on the market. The TaxoBank database and the WAND catalog are a great place to start looking for existing taxonomies that you can use to get started. The advantage here is that you don’t have to start from scratch. However, this could result in a taxonomy that is more generalized or complex than what you need. 

You can also create your own taxonomy starting from your own content. This could be a good option if you’re doing business in a specialized area with very domain-specific language. Doing so would require considerable subject matter expertise and time to build it from scratch. 

Luckily, there are solutions that support every scenario. 

The expert.ai platform leverages semantic tools and an out-of-the-box knowledge graph to build a taxonomy from your own content, ensuring that every piece of content is accurately tagged and connected. As an added layer of intelligence, our industry focused knowledge models for insurance, financial services, life science and media enriches your knowledge even further and accelerates development. These knowledge models are ready to go and easily customized to meet your specific requirements. Finally, we integrate with third-party external knowledge sources—MeSH, ICD9 and ICD10, for example—and integrate with providers like WAND Inc. to provide end to end solutions fit for any use case. 

Taxonomy Management: Next Steps

Now that you understand what taxonomies are and how you should use them, here are some resources to help you take the next steps in taxonomy development and management:

Following the Taxonomy Roadmap to Data Discovery Success

Data discovery is the process of uncovering insights lying dormant in your enterprise – creating value from your data and getting it to the business users who need it, when they need it.

In this white paper, we’ll show you why taxonomies are the solution to your data discovery challenges and share important recommendations to guide you on your journey.

Download the White Paper
Following the Taxonomy Roadmap to Data Discovery Success