Hi and welcome. We’ve previously explored how to define a taxonomy and now in this video, we're diving into what makes an essential strategy for effective taxonomy optimisation.

We know there’s no one-size-fits-all technique that guarantees the perfect balance to achieving optimal taxonomy. Instead, the most effective strategy is adopting an iterative approach.

The iterative approach involves a cyclical process of refining your taxonomy through continuous rounds of evaluation, adjustment and testing. This method acknowledges the complexity of taxonomy optimisation and embraces ongoing refinement based on feedback and insights. Let’s explore these three steps of iteration and how they apply to taxonomy.

It’s important that you evaluate your current taxonomy using real-world data and performance metrics to identify areas for improvement and specific challenges. You should also adjust and make necessary changes to the taxonomy. This could mean revising class names, merging or splitting classes or refining semantic relationships.

Then, when testing the updated taxonomy, you can evaluate the impact of changes on classification accuracy and efficiency.This iterative approach allows you to respond to evolving requirements and challenges as they arise, creating a systematic and pragmatic framework to navigate the complexities of taxonomy optimisation, continually improving until an optimal balance is achieved.

Before diving into your first iteration, it's crucial to conduct a comprehensive semantic analysis of your taxonomy. This foundational step identifies potential areas for refinement, ensuring semantic distinctness in class naming. It’s important to evaluate each class name for semantic coherence with its corresponding document content to ensure names accurately reflect the scope of the documents. You should also identify any potential conflicts or redundancies between class names and ensure they are complementary and mutually exclusive. Lastly, be sure to collaborate with domain experts or end-users to validate the semantic accuracy and relevance of class names.

It’s this analysis that lays the groundwork for effective iterations, enhancing clarity and specificity in your taxonomy. For the first iteration you should always run a predetermined number of files through the initial taxonomy and use this as your starting point. Some possible learnings from this iteration could be that files may be classified as "Other" which indicate gaps in the taxonomy or the need for class name adjustments. Seeing confused classes suggests they are not distinct enough and may need merging or renaming. Any unused classes might be candidates for removal or renaming. You can also identify areas needing focus by assessing overall accuracy and accuracy per class.

In subsequent iterations you can focus on optimising to meet a target accuracy of 70%, using your learnings from previous iterations and continue refining your taxonomy based on feedback and performance metrics.

The advantages of taking this iterative approach are:

Increased efficiency - as this reduces the risk of re-work from iteration to iteration.

Gap Analysis - as this provides an upfront understanding of the gap between current and target accuracy.

Targeted Optimisation - as this focuses iterations on specific areas needing improvement.

Adaptability - as it addresses key complexities early, allowing more time for adjustments if issues arise.

By embracing an iterative approach to taxonomy optimisation, you can systematically navigate the complexities of balancing class number, distinct naming and semantic alignment, ultimately leading to a well-optimised and effective taxonomy.

‍