## 4 Discussion
Within the timeline we examined, ML and DL works related to ontologies and KGs emerge in 2017 for entity extraction tasks (NER for brevity) and in 2019 for other tasks such as link prediction. A close examination of Table 6 reveals that evaluation metrics for NER fluctuate depending on the task and dataset on which a proposed model was trained; however, the emerging use of pretrained models such as BERT has generally led to superior performance, with precision, recall, and F1-scores often exceeding 90% [92, 93, 97]. Notably, BERT has demonstrated satisfactory performance on NER tasks from Chinese reports, achieving scores greater than 80% in the aforementioned metrics. Thus, the use of pretrained models can not only reduce training times but also significantly enhance the performance of NER tasks across different languages. An interesting pattern emerges when comparing the performance of models in entity extraction and relation extraction. According to reported results (Table 6), models generally perform better in NER than in relation extraction tasks, as indicated by the significant drop in recall and F1-score for relation extraction. However, precision in relation extraction is significantly higher than both recall and F1-score, and is either on par with [29] or higher than [100] in terms of precision in NER. This discrepancy is likely because relation extraction is a more complex task due to the difficulty of accurately capturing semantic relationships. Consequently, models become more conservative in predicting relations, reporting only those they deem most probable. This behavior reduces the occurrence of false positives, leading to higher precision, but results in missed true relations, leading to lower recall and consequently lower F1-scores. Conversely, the abundance of entities in cybersecurity text data makes identification of entities easier than the task of extracting their relationships. A final and noteworthy remark concerns the influence of adversarial attacks on model performance and the validity of available data. As demonstrated by [PERSON] et al. [112], supervised classifiers such as RF and GBoost can be severely affected by adversaries in both the training and testing phases, raising concerns about the robustness of many proposed models. Furthermore, the systematic use of LLMs presents a double-edged sword. As [PERSON] et al. [111] have demonstrated, fake data that closely mimic real data can be generated to poison datasets, posing a significant threat to data integrity. To conclude, despite the prominence of ML and DL in research on more elaborate inference techniques, works developing \"less fancy\" methods such as description logic-based or similarity measure-based inference remain appealing to researchers and produce considerable results. Finally, we would like to highlight the importance of unifying the proposed ontologies and KGs into a common representation that encapsulates all knowledge extracted from different studies. In this way, more complex and previously unseen patterns and relationships can be potentially unveiled when applying inference and link prediction techniques on all the knowledge encoded. Meanwhile, research should also aim to discover methods that shield the frameworks and the available data from possible adversarial attacks.