[17] Matthew Horridge, Simon Jupp, Georgina Moulton, Alan Rector, Robert Stevens, and Chris Wroe. A practical guide to building OWL Ontologies using Protégé 4 and CO-ODE Tools Edition 1.2. The University of Manchester, 2009.
[18] Ian Horrocks, Peter F Patel-Schneider, Harold Boley, Said Tabet, Benjamin Grosof, Mike Dean, et al. SWRL: A semantic web rule language combining OWL and RuleML. W3C Member submission, 21(79):1-31, 2004.
[19] Drew A Hudson and Christopher D Manning. Compositional attention networks for machine reasoning. arXiv preprint arXiv:1803.03067, 2018.
[20] Drew A Hudson and Christopher D Manning. GQA: A new dataset for real-world visual reasoning and compositional question answering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6700-6709, 2019.
[21] Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2901–2910, 2017.
[22] Jean-Baptiste Lamy. Owlready: Ontology-oriented programming in python with automatic classification and high level constructs for biomedical ontologies. Artificial intelligence in medicine, 80:11-28, 2017.
[23] Feng Li, Hao Zhang, Yi-Fan Zhang, Shilong Liu, Jian Guo, Lionel M Ni, PengChuan Zhang, and Lei Zhang. Vision-language intelligence: Tasks, representation learning, and large models. arXiv preprint arXiv:2203.01922, 2022.
[24] Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang. VisualBert: A simple and performant baseline for vision and language. arXiv preprint arXiv:1908.03557, 2019.
[25] Zechen Li and Anders Søgaard. Qlevr: A diagnostic dataset for quantificational language and elementary visual reasoning. arXiv preprint arXiv:2205.03075, 2022.
[26] Pan Lu, Swaroop Mishra, Tony Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan. Learn to explain: Multimodal reasoning via thought chains for science question answering. In The 36th Conference on Neural Information Processing Systems (NeurIPS), 2022.
[27] Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B Tenenbaum, and Jiajun Wu. The neurosymbolic concept learner: Interpreting scenes, words, and sentences from natural supervision. arXiv preprint arXiv:1904.12584, 2019.
[28] Kenneth Marino, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi. Ok-VQA: A visual question answering benchmark requiring external knowledge. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3195-3204, 2019.
[29] Deborah L McGuinness, Frank Van Harmelen, et al. OWL web ontology language overview. W3C recommendation, 10(10):2004, 2004.
[30] Eric Prud'hommeaux and Andy Seaborne. SPARQL Query Language for RDF. W3C Recommendation, 2008.
[31] Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, Kenneth Marino, and Roozbeh Mottaghi. A-okvqa: A benchmark for visual question answering using world knowledge. In European Conference on Computer Vision, pages 146–162. Springer, 2022.
[32] Hao Tan and Mohit Bansal. LXMERT: Learning cross-modality encoder representations from transformers. arXiv preprint arXiv:1908.07490, 2019.
[33] Peng Wang, Qi Wu, Chunhua Shen, Anthony Dick, and Anton Van Den Hengel. FVQA: Fact-based visual question answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(10):2413-2427, 2017.