{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T05:15:25Z","timestamp":1755839725124,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T00:00:00Z","timestamp":1717027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,30]]},"DOI":"10.1145\/3652583.3658040","type":"proceedings-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T06:30:40Z","timestamp":1717741840000},"page":"1051-1060","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["G-SAP: Graph-based Structure-Aware Prompt Learning over Heterogeneous Knowledge for Commonsense Reasoning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8944-6759","authenticated-orcid":false,"given":"Ruiting","family":"Dai","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China, ChengDu, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0693-5230","authenticated-orcid":false,"given":"Yuqiao","family":"Tan","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, ChengDu, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4742-4456","authenticated-orcid":false,"given":"Lisi","family":"Mo","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, ChengDu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7387-2801","authenticated-orcid":false,"given":"Shuang","family":"Liang","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, ChengDu, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8816-7123","authenticated-orcid":false,"given":"Guohao","family":"Huo","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, ChengDu, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5742-3589","authenticated-orcid":false,"given":"Jiayi","family":"Luo","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, ChengDu, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5064-8786","authenticated-orcid":false,"given":"Yao","family":"Cheng","sequence":"additional","affiliation":[{"name":"University of Electronic Science and Technology of China, ChengDu, China"}]}],"member":"320","published-online":{"date-parts":[[2024,6,7]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"35","author":"Bian Ning","year":"2021","unstructured":"Ning Bian, Xianpei Han, Bo Chen, and Le Sun. 2021. Benchmarking knowledgeenhanced commonsense question answering via knowledge-to-text transformation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 12574--12582."},{"key":"e_1_3_2_1_2_1","volume-title":"Jianfeng Gao, and Yejin Choi.","author":"Bisk Yonatan","year":"2020","unstructured":"Yonatan Bisk, Rowan Zellers, Ronan Le Bras, Jianfeng Gao, and Yejin Choi. 2020. PIQA: Reasoning about Physical Commonsense in Natural Language. (2020), 7432--7439."},{"unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020) 1877--1901.","key":"e_1_3_2_1_3_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_4_1","DOI":"10.18653\/v1\/2020.coling-main.232"},{"key":"e_1_3_2_1_5_1","volume-title":"Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio.","author":"Cho Kyunghyun","year":"2014","unstructured":"Kyunghyun Cho, Bart Van Merri\u00ebnboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_6_1","DOI":"10.1609\/aimag.v41i4.5304"},{"key":"e_1_3_2_1_7_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. CoRR abs\/1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina N Toutanova. 2018. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. CoRR abs\/1810.04805 (2018)."},{"doi-asserted-by":"crossref","unstructured":"Yanlin Feng Xinyue Chen??\u00f2 Bill Yuchen Lin Peifeng Wang Jun Yan and Xiang Ren. 2020. Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering. (2020) 1295--1309.","key":"e_1_3_2_1_8_1","DOI":"10.18653\/v1\/2020.emnlp-main.99"},{"doi-asserted-by":"crossref","unstructured":"Mor Geva Yoav Goldberg and Jonathan Berant. 2019. Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets. (2019) 1161--1166.","key":"e_1_3_2_1_9_1","DOI":"10.18653\/v1\/D19-1107"},{"doi-asserted-by":"publisher","unstructured":"Maarten Grootendorst. 2020. KeyBERT: Minimal keyword extraction with BERT. https:\/\/doi.org\/10.5281\/zenodo.4461265","key":"e_1_3_2_1_10_1","DOI":"10.5281\/zenodo.4461265"},{"key":"e_1_3_2_1_11_1","first-page":"15908","article-title":"Transformer in transformer","volume":"34","author":"Han Kai","year":"2021","unstructured":"Kai Han, An Xiao, Enhua Wu, Jianyuan Guo, Chunjing Xu, and Yunhe Wang. 2021. Transformer in transformer. Advances in Neural Information Processing Systems 34 (2021), 15908--15919.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_12_1","volume-title":"Proceedings, Part XXVIII (Lecture Notes in Computer Science","volume":"73","author":"He Tao","year":"2022","unstructured":"Tao He, Lianli Gao, Jingkuan Song, and Yuan-Fang Li. 2022. Towards Open- Vocabulary Scene Graph Generation with Prompt-Based Finetuning. In Computer Vision - ECCV 2022 - 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XXVIII (Lecture Notes in Computer Science, Vol. 13688), Shai Avidan, Gabriel J. Brostow, Moustapha Ciss\u00e9, Giovanni Maria Farinella, and Tal Hassner (Eds.). Springer, 56--73."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_13_1","DOI":"10.1109\/TNNLS.2021.3129280"},{"key":"e_1_3_2_1_14_1","volume-title":"Parameter-Efficient Transfer Learning for NLP. 97","author":"Houlsby N","year":"2019","unstructured":"N Houlsby, A Giurgiu, S Jastrzkebski, B Morrone, Q de Laroussilhe, A Gesmundo, M Attariyan, and S Gelly. 2019. Parameter-Efficient Transfer Learning for NLP. 97 (2019), 2790--2799."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_15_1","DOI":"10.18653\/v1\/2023.acl-long.750"},{"key":"e_1_3_2_1_16_1","volume-title":"Proceedings of naacL-HLT","volume":"1","author":"Ming-Wei Chang Jacob Devlin","year":"2019","unstructured":"Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of naacL-HLT, Vol. 1. 2."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_17_1","DOI":"10.18653\/v1\/2020.findings-emnlp.171"},{"key":"e_1_3_2_1_18_1","volume-title":"International Conference on Machine Learning. PMLR, 5583--5594","author":"Kim Wonjae","year":"2021","unstructured":"Wonjae Kim, Bokyung Son, and Ildoo Kim. 2021. Vilt: Vision-and-language transformer without convolution or region supervision. In International Conference on Machine Learning. PMLR, 5583--5594."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_19_1","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"e_1_3_2_1_20_1","volume-title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.","author":"Lewis Mike","year":"2020","unstructured":"Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2020. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. (2020), 7871--7880."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_21_1","DOI":"10.18653\/v1\/2021.acl-long.353"},{"unstructured":"Bill Yuchen Lin Xinyue Chen Jamin Chen and Xiang Ren. 2019. KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning. (2019) 2829-- 2839.","key":"e_1_3_2_1_22_1"},{"key":"e_1_3_2_1_23_1","volume-title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach. CoRR abs\/1907.11692","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. CoRR abs\/1907.11692 (2019)."},{"key":"e_1_3_2_1_24_1","volume-title":"Chandra Bhagavatula, and Yejin Choi.","author":"Lourie Nicholas","year":"2021","unstructured":"Nicholas Lourie, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2021. UNICORN on RAINBOW: A Universal Commonsense Reasoning Model on a New Multitask Benchmark. (2021), 13480--13488."},{"doi-asserted-by":"crossref","unstructured":"Shangwen Lv Daya Guo Jingjing Xu Duyu Tang Nan Duan Ming Gong Linjun Shou Daxin Jiang Guihong Cao and Songlin Hu. 2020. Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering. (2020) 8449--8456.","key":"e_1_3_2_1_25_1","DOI":"10.1609\/aaai.v34i05.6364"},{"key":"e_1_3_2_1_26_1","volume-title":"Matsumoto","author":"David","year":"2009","unstructured":"David Ed Matsumoto. 2009. The Cambridge dictionary of psychology. Cambridge University Press."},{"key":"e_1_3_2_1_27_1","volume-title":"AntoineBosselut Percy Liang, and Jure Leskovec","author":"MichihiroYasunaga HongyuRen","year":"2021","unstructured":"HongyuRen MichihiroYasunaga, AntoineBosselut Percy Liang, and Jure Leskovec. 2021. QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering. (2021), 535--546."},{"doi-asserted-by":"crossref","unstructured":"Todor Mihaylov Peter Clark Tushar Khot and Ashish Sabharwal. 2018. Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering. (2018) 2381--2391.","key":"e_1_3_2_1_28_1","DOI":"10.18653\/v1\/D18-1260"},{"doi-asserted-by":"crossref","unstructured":"Adam Poliak Jason Naradowsky Aparajita Haldar Rachel Rudinger and Benjamin Van Durme. 2018. Hypothesis Only Baselines in Natural Language Inference. (2018) 180--191.","key":"e_1_3_2_1_29_1","DOI":"10.18653\/v1\/S18-2023"},{"unstructured":"Alec Radford Karthik Narasimhan Tim Salimans Ilya Sutskever et al. 2018. Improving Language Understanding by Generative Pre-Training. OpenAI.","key":"e_1_3_2_1_30_1"},{"unstructured":"Alec Radford JeffreyWu Rewon Child David Luan Dario Amodei Ilya Sutskever et al. 2019. Language models are unsupervised multitask learners. OpenAI blog 1 8 (2019) 9.","key":"e_1_3_2_1_31_1"},{"key":"e_1_3_2_1_32_1","volume-title":"A simple neural network module for relational reasoning. Advances in neural information processing systems 30","author":"Santoro Adam","year":"2017","unstructured":"Adam Santoro, David Raposo, David G Barrett, Mateusz Malinowski, Razvan Pascanu, Peter Battaglia, and Timothy Lillicrap. 2017. A simple neural network module for relational reasoning. Advances in neural information processing systems 30 (2017)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_33_1","DOI":"10.5555\/3298023.3298212"},{"unstructured":"Yueqing Sun Qi Shi and Yu Zhang Le Qi. 2022. JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answering. (2022) 5049--5060.","key":"e_1_3_2_1_34_1"},{"key":"e_1_3_2_1_35_1","volume-title":"COMMONSENSEQA: A Question Answering Challenge Targeting Commonsense Knowledge.","author":"Talmor Alon","year":"2019","unstructured":"Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019. COMMONSENSEQA: A Question Answering Challenge Targeting Commonsense Knowledge. (2019), 4149--4158."},{"key":"e_1_3_2_1_36_1","volume-title":"Modeling Relational Data with Graph Convolutional Networks. 10843","author":"van den Berg Rianne","year":"2018","unstructured":"Rianne van den Berg, Ivan Titov, and Max Welling. 2018. Modeling Relational Data with Graph Convolutional Networks. 10843 (2018), 593--607."},{"key":"e_1_3_2_1_37_1","volume-title":"Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering. EMNLP 2020","author":"Wang Peifeng","year":"2020","unstructured":"Peifeng Wang, Nanyun Peng, Filip Ilievski, Pedro Szekely, and Xiang Ren. 2020. Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering. EMNLP 2020 (2020), 4129--4140."},{"key":"e_1_3_2_1_38_1","volume-title":"CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering. arXiv preprint arXiv:2305.14869","author":"Wang Weiqi","year":"2023","unstructured":"Weiqi Wang, Tianqing Fang, Wenxuan Ding, Baixuan Xu, Xin Liu, Yangqiu Song, and Antoine Bosselut. 2023. CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering. arXiv preprint arXiv:2305.14869 (2023)."},{"doi-asserted-by":"crossref","unstructured":"Xiaoyan Wang Pavan Kapanipathi Ryan Musa Mo Yu Kartik Talamadupula Ibrahim Abdelaziz Maria Chang Achille Fokoue Bassem Makni Nicholas Mattei et al. 2019. Improving Natural Language Inference Using External Knowledge in the Science Questions Domain. (2019) 7208--7215.","key":"e_1_3_2_1_39_1","DOI":"10.1609\/aaai.v33i01.33017208"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_40_1","DOI":"10.18653\/v1\/2022.acl-long.292"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_41_1","DOI":"10.18653\/v1\/2023.acl-long.785"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_42_1","DOI":"10.1145\/1321440.1321449"},{"key":"e_1_3_2_1_43_1","first-page":"1201","article-title":"Fusing Context Into Knowledge Graph for Commonsense Question Answering","volume":"2021","author":"Xu Yichong","year":"2021","unstructured":"Yichong Xu, Chenguang Zhu, Ruochen Xu, Yang Liu, Michael Zeng, and Xuedong Huang. 2021. Fusing Context Into Knowledge Graph for Commonsense Question Answering. In Findings of the Association for Computational Linguistics: ACLIJCNLP 2021. 1201--1207.","journal-title":"Findings of the Association for Computational Linguistics: ACLIJCNLP"},{"key":"e_1_3_2_1_44_1","volume-title":"Xlnet: Generalized autoregressive pretraining for language understanding. Advances in neural information processing systems 32","author":"Yang Zhilin","year":"2019","unstructured":"Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019. Xlnet: Generalized autoregressive pretraining for language understanding. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_1_45_1","first-page":"37309","article-title":"Deep bidirectional language knowledge graph pretraining","volume":"35","author":"Yasunaga Michihiro","year":"2022","unstructured":"Michihiro Yasunaga, Antoine Bosselut, Hongyu Ren, Xikun Zhang, Christopher D Manning, Percy S Liang, and Jure Leskovec. 2022. Deep bidirectional language knowledge graph pretraining. Advances in Neural Information Processing Systems 35 (2022), 37309--37323.","journal-title":"Advances in Neural Information Processing Systems"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_46_1","DOI":"10.1111\/mice.12954"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_47_1","DOI":"10.1016\/j.eswa.2023.120286"},{"key":"e_1_3_2_1_48_1","volume-title":"Nikita Bhutani, and Isabelle Augenstein.","author":"Zheng Chen","year":"2022","unstructured":"Chen Zheng, Parisa Kordjamshidi, Sagnik Ray Choudhury, Nikita Bhutani, and Isabelle Augenstein. 2022. Dynamic Relevance Graph Network for Knowledge- Aware Question Answering. (2022), 1357--1366."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_49_1","DOI":"10.1007\/s11263-022-01653-1"}],"event":{"sponsor":["SIGMM ACM Special Interest Group on Multimedia","SIGSOFT ACM Special Interest Group on Software Engineering"],"acronym":"ICMR '24","name":"ICMR '24: International Conference on Multimedia Retrieval","location":"Phuket Thailand"},"container-title":["Proceedings of the 2024 International Conference on Multimedia Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652583.3658040","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3652583.3658040","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T08:54:34Z","timestamp":1755766474000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3652583.3658040"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,30]]},"references-count":49,"alternative-id":["10.1145\/3652583.3658040","10.1145\/3652583"],"URL":"https:\/\/doi.org\/10.1145\/3652583.3658040","relation":{},"subject":[],"published":{"date-parts":[[2024,5,30]]},"assertion":[{"value":"2024-06-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}