The primary goal of classification is to provide a systematic and efficient way of organizing information, such that it can be easily retrieved, searched, and understood. Classification in information science plays a crucial role in organizing and managing large volumes of information, enabling efficient access, retrieval, and analysis, and ultimately helping users make sense of complex datasets. Custom taxonomies can significantly improve findability by providing a more specific and tailored classification system for organizing and categorizing information. They allow for more detailed and specific classification of content. By creating additional custom categories or tags, you can capture nuances and characteristics that might not be covered by generic or pre-existing taxonomies. This increased granularity helps users locate content that aligns with their specific needs or interests.

Search has become more intelligent, personalized and diverse, leveraging technologies to deliver faster and more accurate results across a wide range of platforms and devices. Making the content findable is important to knowledge management.

Metadata makes digital content findable. Findability, however, works only when a proper taxonomy is in place.

By defining a set of taxonomy terms and applying them consistently across your content, you create a coherent framework that makes it easier for users to understand and navigate your information. This consistency enhances findability by reducing confusion and creating a logical structure that aligns with users’ mental models.

The biggest challenge is that most organizations have little knowledge on how AI systems make decisions and how to interpret AI and machine learning results. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and its potential biases.

Custom taxonomies offer a flexible and customizable approach to organizing and classifying information, which improves findability by providing more specific categorization, context, and personalized experiences for users.

Data Harmony is a fully customizable suite of software products designed to maximize precise and efficient information management and retrieval. Our suite includes tools for taxonomy and thesaurus construction, machine aided indexing, database management, information retrieval, and explainable artificial intelligence.

Melody K. Smith

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

Sponsored by Access Innovations, changing search to found.