1. Álvez, J., Lucio, P., Rigau, G.: Adimen-SUMO: Reengineering an ontology for first-order reasoning. Int. J. Semant. Web Int. Syst. (IJSWIS) 8(4), 80–116 (2012), https://ideas.repec.org/a/igg/jswis0/v8y2012i4p80-116.html
2. Barredo Arrieta, A., et al.: Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 58, 82–115 (2020). https://doi.org/10.1016/j.inffus.2019.12.012
3. Brown, T., et al.: Language models are few-shot learners. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems 33 (NeurIPS 2020), pp. 1877–1901 (2020). https://proceedings.neurips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf
4. Burkart, N., Huber, M.F.: A survey on the explainability of supervised machine learning. J. Artif. Intell. Res. 70, 245–317 (2021). https://doi.org/10.1613/jair.1.12228
5. Chen, M., D’arcy, M., Liu, A., Fernandez, J., Downey, D.: CODAH: An adversarially-authored question answering dataset for common sense. In: Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP, pp. 63–69 (2019). https://www.jaredfern.com/publication/codah/