References

  1. (1991): Symbolic and Neural Learning Algorithms: An Experimental Comparison. Machine Learning 6(2), pp. 111–143, doi:10.1023/A:1022602303196.
  2. Leo Breiman (2001): Random Forests. Machine Learning 45(1), pp. 5–32, doi:10.1023/A:1010933404324.
  3. Carla Zoe Cremer (2021): Deep limitations? Examining expert disagreement over deep learning. Progress in Artificial Intelligence, doi:10.1007/s13748-021-00239-1.
  4. Luc De Raedt, Sebastijan Dumanči\'c, Robin Manhaeve & Giuseppe Marra (2020): From statistical relational to neuro-symbolic artificial intelligence. arXiv preprint arXiv:2003.08316. Available at https://arxiv.org/abs/2003.08316.
  5. Derek Doran, Sarah Schulz & Tarek R. Besold (2017): What Does Explainable AI Really Mean? A New Conceptualization of Perspectives. CoRR abs/1710.00794. Available at http://arxiv.org/abs/1710.00794.
  6. Matthias Fey & Jan Eric Lenssen (2019): Fast Graph Representation Learning with PyTorch Geometric.
  7. Artur d'Avila Garcez & Luis C Lamb (2020): Neurosymbolic AI: The 3rd Wave. arXiv e-prints, pp. arXiv–2012.
  8. Jiuxiang Gu, Zhenhua Wang, Jason Kuen, Lianyang Ma, Amir Shahroudy, Bing Shuai, Ting Liu, Xingxing Wang, Gang Wang, Jianfei Cai & Tsuhan Chen (2018): Recent advances in convolutional neural networks. Pattern Recognition 77, pp. 354–377, doi:10.1016/j.patcog.2017.10.013. Available at https://www.sciencedirect.com/science/article/pii/S0031320317304120.
  9. Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta & Jure Leskovec (2021): Open Graph Benchmark: Datasets for Machine Learning on Graphs. Available at https://arxiv.org/abs/2005.00687.
  10. Nils M. Kriege, Fredrik D. Johansson & Christopher Morris (2020): A survey on graph kernels. Applied Network Science 5(1), pp. 6, doi:10.1007/s41109-019-0195-3.
  11. Luis Lamb, Artur Garcez, Marco Gori, Marcelo Prates, Pedro Avelar & Moshe Vardi (2020): Graph neural networks meet neural-symbolic computing: A survey and perspective. arXiv preprint arXiv:2003.00330. Available at https://arxiv.org/abs/2003.00330.
  12. Quoc Le & Tomas Mikolov (2014): Distributed Representations of Sentences and Documents. In: Proceedings of The 31st International Conference on Machine Learning, pp. 1188–1196.
  13. Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard & L. D. Jackel (1989): Backpropagation Applied to Handwritten Zip Code Recognition. Neural Computation 1, pp. 541–551, doi:10.1162/neco.1989.1.4.541.
  14. Rada Mihalcea & Paul Tarau (2004): TextRank: Bringing Order into Texts. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2004), Barcelona, Spain.
  15. Mateusz Pawlik & Nikolaus Augsten (2015): Efficient Computation of the Tree Edit Distance. ACM Trans. Database Syst. 40(1), pp. 3:1–3:40, doi:10.1145/2699485.
  16. Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton & Christopher D. Manning (2020): Stanza: A Python Natural Language Processing Toolkit for Many Human Languages. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, doi:10.18653/v1/2020.acl-demos.14. Available at https://nlp.stanford.edu/pubs/qi2020stanza.pdf.
  17. Anida Sarajlic, Noel Malod-Dognin, Omer Nebil Yaveroglu & Natasa Przulj (2016): Graphlet-based Characterization of Directed Networks. Scientific Reports 6, pp. 35098, doi:10.1038/srep35098.
  18. P. Smolensky (1987): Connectionist AI, symbolic AI, and the brain. Artificial Intelligence Review 1(2), pp. 95–109, doi:10.1007/BF00130011.
  19. Paul Tarau (2021): Natlog: a Lightweight Logic Programming Language with a Neuro-symbolic Touch. In: Andrea Formisano, Yanhong Annie Liu, Bart Bogaerts, Alex Brik, Veronica Dahl, Carmine Dodaro, Paul Fodor, Gian Luca Pozzato, Joost Vennekens & Neng-Fa Zhou: Proceedings 37th International Conference on Logic Programming (Technical Communications) , 20-27th September 2021.
  20. Paul Tarau & Eduardo Blanco (2020): Interactive Text Graph Mining with a Prolog-Based Dialog Engine. Theory and Practice of Logic Programming, pp. 1–20, doi:10.1017/S1471068420000137.
  21. Jan Wielemaker, Tom Schrijvers, Markus Triska & Torbjorn Lager (2012): SWI-Prolog. Theory and Practice of Logic Programming 12, pp. 67–96, doi:10.1017/S1471068411000494.
  22. Wikipedia (2021): Jaccard index — Wikipedia, The Free Encyclopedia. Available at https://en.wikipedia.org/wiki/Jaccard_index. [Online; accessed 09-October-2021].
  23. Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu & Maosong Sun (2021): Graph Neural Networks: A Review of Methods and Applications. CoRR abs/1812.08434. Available at http://arxiv.org/abs/1812.08434.

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