# Semi-Supervised Taxonomy Expansion and Completion in Dynamic Taxonomies
## 5 Discussion and Analysis  
The results obtained from the RAG-based classifier provide valuable insights into its performance when classifying skills into hypernyms and hyponyms within a dynamic taxonomy. With an overall accuracy of 0.78, the classifier demonstrates strong general effectiveness, indicating that its predictions align closely with the ground truth in the majority of cases. Nonetheless, performance variations across different labels highlight both strengths and limitations. The classifier achieved a weighted precision of 0.79 and a weighted recall of 0.78, suggesting consistent and balanced performance across most labels, especially considering the underlying distribution. The balanced nature of these metrics is further confirmed by the weighted F1 score of 0.77, which combines precision and recall into a unified measure of reliability and robustness across the entire set of categories. However, a macro F1 score of 0.64 indicates notable disparities in the model's performance among individual labels, particularly affecting less frequently occurring categories such as \"new hypernym\". The lower performance observed in these minority classes highlights challenges associated with imbalanced datasets and underscores the complexity involved in correctly identifying and classifying new or evolving concepts. The confusion matrix presented in Figure 5 provides further clarity on specific misclassification tendencies by illustrating the classification results across the three target labels: existing hyponym, new hyponym, and new hypernym. The matrix visually represents the distribution of correct and incorrect predictions, with the diagonal cells indicating correct \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Class Label** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Existing hyponym & 0.81 & 0.82 & 0.81 & 150 \\ New hypernym & 1.00 & 0.20 & 0.33 & 20 \\ New hyponym & 0.74 & 0.81 & 0.78 & 150 \\ Macro Avg & 0.85 & 0.61 & 0.64 & 320 \\ Weighted Avg & 0.79 & 0.78 & 0.77 & 320 \\ \hline Accuracy & & & 0.78 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification metrics for each label and overall system performance. classifications. The matrix illustrates a pronounced bias toward classifying concepts as \"existing hyponyms\" rather than correctly identifying \"new hyponyms\" and \"new hypernym\" categories. This inclination suggests that the model currently struggles to distinguish subtle differences between variations of existing terms and entirely new concepts. Such misclassifications underline the necessity for enhanced feature engineering or the incorporation of richer contextual information to boost differentiation capabilities. Overall, while the RAG-based classifier exhibits promising capabilities for horizontal taxonomy expansion tasks, its performance in vertical taxonomy classifications requires further development. The limitations, particularly for underrepresented classes like \"new hypernym\", highlight the importance of addressing data imbalance and refining the model's capacity to recognize novel or less represented terms effectively. Beyond the technical results, this framework holds significant implications for education, especially in how academic institutions design and update their curricula. The ability to dynamically expand and complete taxonomies allows educational programs to remain closely aligned with industry needs, incorporating emerging skills such as \"Prompt Engineering\" or \"Carbon-Neutral Technologies\" into course offerings in a timely manner. This responsiveness is vital in rapidly evolving sectors, where traditional curriculum revision cycles often lag behind technological advancement. Learners benefit from exposure to current and market-relevant competencies, educators are supported in identifying skill gaps and content updates, and institutions enhance their capacity to deliver impactful upskilling and reskilling programs.