Feeling overwhelmed by content management? Taxonomy can help.
But what is taxonomy?
Taxonomy identifies and classifies information into a hierarchical structure so it can be analyzed. But taxonomy isn’t new. It was used as early as 300 BCE in ancient Greece to classify plants.
Explorers in the 16th and 17th centuries used taxonomy to categorize the plant and animal species collected during expeditions. More than 8.7 million species of plants and animals are in existence today. So, it’s easy to see how helpful taxonomies would be to categorize this information.
In the digital world, taxonomy applies structure to content components 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 in much the same way that plant and animal species are managed by modern scientists.
Taxonomy in the Digital Age
It’s no surprise that taxonomy has stood the test of time. Today, taxonomy as a means to organize and manage content is groundbreaking. When combined with natural language processing (NLP), it helps breakdown, tag, and classify natural language.
Consider the challenge for businesses to manage large quantities of text-based documents, web pages, social media, news articles, and other language-based assets. There are 7.5 septillion gigabytes of data being generated every day. As much as 85% of content is considered dark data because it’s not discoverable. AI and taxonomy make content discoverable for machines and users.
Categorize With NLP and Taxonomy
NLU programs with taxonomy capabilities, like the expert.ai Platform, enable an automated, AI-driven solution to classify and tag content metadata at scale. It brings content out of the shadows by providing hierarchical context to content terms. This is in addition to analyzing content, identifying keywords, and organizing, and tagging words and phrases.
To better understand the value of taxonomy for content categorization consider the challenge of categorizing legal and medical documents. This content includes complex language with proprietary terms. With a customizable taxonomy it’s possible to classify and tag niche terms thereby automating document classification.
How is Taxonomy Determined?
Taxonomy design starts by collecting the lowest level terms and extracting them into predefined categories. This is called Named Entity Recognition (NER). NER is a type of document analysis that uses entity extraction to detect and categorize key elements from text.
NER can discover elements from raw data. It then determines a category based on the taxonomy that has been applied. Taxonomy makes NER models, such as expert.ai’s, more accurate by annotating content with labels so more precise clusters can be returned.
For the NER model to be accurate, it needs to be repeatedly trained to identify the most relevant terms and concepts.
Why Use Taxonomies?
Now that we understand the role taxonomy plays in a language model, we can explore how it adds value for the end user. Some common enterprise uses include:
- Grouping, categorizing, and organizing content.
- Making content searchable and retrievable.
- Finding correlations between content.
- Improving user experiences with content.
- Reducing the amount of time spent managing content.
- Tracking and managing content lifecycles.
Benefits of Programmatic Taxonomy
Using a natural language platform automates content processing by tagging and classifying content according to a customized taxonomy. The expert.ai platform programmatically discovers taxonomies and tags and classifies content by a topic or customized taxonomy.
Programmatic taxonomy makes it possible to automatically process content, documents, web pages, social media, news articles, and other language-based assets. It can also be used to do the following:
- Analyze documents with NER.
- Classify documents based on taxonomy categories.
- Detect and extract information from text.
For companies that have complex documents, this classification system is invaluable. For example, an insurance company can use taxonomy technology to read and understand the complex language in insurance documents.
In this case, taxonomy classifies and tags information like coverage, exclusions, and endorsements. It is then extracted from the document and classified according to a predetermined taxonomy.
Taxonomy with expert.ai’s capabilities makes it easy for users to access and consume content. It provides the ability to uncover value and connections across taxonomies with elements that enhance discoverability by users and machines.
Expert.ai’s categorization, data linking, and entity extraction provide content structure based on taxonomies to speed content search and discovery, at scale. News outlets rely on this technology to classify 1.5 million articles per day. Yet any sized business can benefit from this taxonomy approach to make content discoverable and categorizable so it can add value to their business.
A little organization in your unstructured data goes a long way. Put taxonomy to use in your organization.