In the ever-evolving landscape of information management, custom taxonomies have long served as a vital tool for organizing and retrieving vast amounts of data. Traditionally, these taxonomies have been manually curated by experts who categorize information into a structured hierarchy based on domain knowledge. However, the advent of artificial intelligence (AI) has introduced new possibilities and challenges to this field. AI is not only automating the creation and maintenance of custom taxonomies but also “enhancing their accuracy, scalability and relevance in ways previously unimaginable” – and they are not yet perfect.

Custom taxonomies enable efficient data retrieval, better content management and enhanced user experience. Creating and maintaining these taxonomies has traditionally been seen as a labor-intensive process, requiring deep domain expertise and constant updates to reflect changes in the field.

One of the most significant challenges AI faces in taxonomy creation is the understanding of context. Taxonomies are not merely lists of categories; they are hierarchies that reflect the nuanced relationships between concepts. AI systems, despite their advanced algorithms, struggle to grasp the subtleties of human language, culture and context.

While AI may offer significant advantages in creating and maintaining custom taxonomies, it also presents certain challenges that organizations must address to fully harness its potential. One of the primary challenges is ensuring the quality and reliability of the AI-generated taxonomies. AI systems are only as good as the data they are trained on. If the training data is biased, incomplete or inaccurate, the resulting taxonomy will reflect these flaws. Organizations must therefore invest in high-quality training data and continuously monitor and evaluate the performance of their AI systems – a labor-intensive process.

Taxonomy creation is as much an art as it is a science. It requires judgment calls that go beyond what an algorithm can provide. For example, when categorizing a new and emerging concept, a human expert might draw on intuition, experience and a deep understanding of the field to place it within the existing taxonomy. An AI, on the other hand, might struggle with novel concepts that do not fit neatly into its pre-existing categories. This can result in taxonomies that are inflexible and unable to adapt to new developments.

At the end of the day, content needs to be findable, and that happens with a strong, standards-based taxonomy. Data Harmony is our patented, award winning, human-in-the-loop (HITL) AI suite that leverages explainable AI for efficient, innovative and precise semantic discovery of your new and emerging concepts, to help you find the information you need when you need it.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.