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  • Writer's pictureShawna Applequist

The Value of Taxonomies in Training AI

Taxonomies: Hierarchical classification structures which are used to organize documents, data, digital assets, and other information for retrieval later.

Ontologies: A representation of concepts and categories belonging to a particular domain and their relationships to each other.

AI (Artificial Intelligence): The ability machines have to learn and make decisions based on analytics and data.

NLP (Natural Language Processing): A branch of computer science and AI which focuses specifically on understanding, or processing, human language as it is written or spoken.

All of these words are particularly hot topics in the world of business and technology right now. But what do they have to do with each other? Taxonomies and ontologies are playing an essential role in the training of Artificial Intelligence (AI) models and engines. This is especially the case when it comes to Natural Language Processing (NLP). Adding a taxonomy/ontology to an AI or NLP project can dramatically increase the speed of training the models and increase the accuracy and therefore efficacy of the model.

While this topic could probably fill several books, today we will be looking at the top five ways in which taxonomies and ontologies add value to training AI systems.

Improved Accuracy: A taxonomy can provide an organized and consistent framework for data categorization within any given system. This consistency and organization helps AI models learn to classify data accurately within the set parameters. Having an ontology allows the AI to understand the relationship between different categories, terms, and synonyms which again, leads to improved accuracy in predictions in classifications.

As an example, please consider an AI model which has been trained to categorize news articles into different topics. This model is trained on a taxonomy/ontology that classifies articles into various categories including things like politics, sports, and entertainment. Because of this system, the AI engine can accurately predict the topic of a new article based on the relationships it has learned between the words used in the article and the matching categories in the taxonomy.

Additionally, taxonomies help eliminate ambiguity in data categorization systems and processes. Ambiguous data is extremely dangerous because it can lead to incorrect classifications and therefore reduce the accuracy of the AI model. Taxonomies and ontologies eliminate this ambiguity by providing clear definitions of categories and informing the AI engine of the relationships between them - thus helping the AI engine make more accurate predictions.

Better Generalization: Ontologies help AI models in generalizing data by learning common relationships and patterns between categories. This is a crucial step that allows AI engines to perform well with unseen data. When a taxonomy is used to train an AI engine, the engine learns relationships through the hierarchical structure, similarities, and differences between the various categories and terms. Once the AI engine is trained with this knowledge, it allows the engine to generalize and therefore make predictions based on previous patterns discovered, even when faced with new data.

For example, let’s pretend we have a taxonomy/ontology that categorizes animals into different classes like mammals, reptiles, birds, etc. The AI engine has been trained on this taxonomy/ontology and as a result, it knows that mammals have specific characteristics like warm-blooded, and reptiles have specific characteristics like cold-blooded. When we feed this model a new animal, it can use this knowledge to make a prediction about which class it belongs to, even if it has not seen this animal before.

Common patterns and relationships are rather intuitive for humans, but AI engines do not have this knowledge unless we train it. Using a taxonomy and ontology helps the AI models to generalize and make more accurate predictions on unseen data, which is crucial.

Reduced Data Bias: Data bias is a significant challenge when training new AI models, and can significantly affect the accuracy of predictions and classifications. There are many ways in which data bias can occur: skewed data, systemic biases, automation biases, selection biases, and overgeneralization biases to name just a few. The biases occur when the data used to train the AI is not representative of the real-world distribution of data. Taxonomies can help reduce data bias by ensuring that the data is evenly distributed across categories.

An example would be an AI model that is trained off of a set of medical documents versus an AI model that is trained off of a medical taxonomy. The medical documents may be reporting data that over-represents the number of cancer patients because the medical documents are from a cancer clinic. This may cause the AI model to improperly conclude that symptoms attributed to various illnesses and diseases are signs of cancer. When the AI is trained with a medical taxonomy that has an even distribution of data, the AI is more likely to draw accurate conclusions about symptoms.

Structured framework is vital for data categorization. It helps ensure that data is evenly distributed across categories which reduces the risk of over-representation in certain categories. Ontologies can help AI systems identify and resolve data bias through the clear representation of relationships between categories.

Better Interpretability: Taxonomies provide a clear and interpretable structure for data categorization, which makes it easier to understand how AI models are choosing to make their predictions. As previously mentioned, humans have a certain level of understanding when it comes to the way the world works which AI models do not intrinsically share. This can lead to confusion if not addressed.

When training an AI model using a taxonomy, the AI can learn about relationships between categories, as we have mentioned before. This knowledge can be used to provide a clear interpretation of the model’s predictions and classifications, which makes it easier to understand how the AI arrived at the decision it did.

Consider an AI model which has been trained to classify images into different categories like animals, vehicles, and buildings. This AI model was trained using a taxonomy that provides clear hierarchies of categories and because of this, the AI can provide an explanation of its predictions by listing the specific subcategory to which it believes the image belongs. Being able to access this data allows the people training the AI model to gain a deeper understanding of the model’s predictions and allow more informed decisions and edits.

Increased Efficiency: Finally, Taxonomies can help speed up the training process by reducing the number of data categories and therefore reducing the complexity of the model which results in faster training times and improved model performance.

Training an AI model on a large dataset is often an extremely time-consuming and computationally intensive process. Taxonomies can help reduce the complexity of this process by allowing the engine to categorize into a premade structured framework, freeing up valuable computation power to focus on the important relationships between categories.

As an example, please consider an AI model that is trained on a taxonomy which categorizes vehicle parts into different classes such as body components, suspension, electronics, etc. Because the AI already has a list of classes, it can focus on learning relationships between those classes rather than trying to learn the relationships between individual parts. This can significantly reduce the amount of data needed to process and the computational resources required.


Taxonomies and ontologies are critical components of efficient, effective AI training, especially when it comes to NLP. They can help improve accuracy and generalization as well as reduce data biases, improve interpretability, and increase efficiency during the training process. By providing structured frameworks for data categorization in these engines, taxonomies are a crucial component of success in AI models.


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