{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:31:14Z","timestamp":1780511474736,"version":"3.54.1"},"reference-count":43,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62222212, 62336001"],"award-info":[{"award-number":["62222212, 62336001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,1,31]]},"abstract":"<jats:p>Despite the emerging research on adversarial attacks against knowledge graph embedding (KGE) models, most of them focus on white-box attack settings. However, white-box attacks are difficult to apply in practice compared to black-box attacks since they require access to model parameters that are unlikely to be provided. In this article, we propose a novel black-box attack method that only requires access to knowledge graph data, making it more realistic in real-world attack scenarios. Specifically, we utilize pre-trained language models (PLMs) to encode text features of the knowledge graphs, an aspect neglected by previous research. We then employ these encoded text features to identify the most influential triples for constructing corrupted triples for the attack. To improve the transferability of the attack, we further propose to fine-tune the PLM model by enriching triple embeddings with structure information. Extensive experiments conducted on two knowledge graph datasets illustrate the effectiveness of our proposed method.<\/jats:p>","DOI":"10.1145\/3688850","type":"journal-article","created":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T15:40:06Z","timestamp":1725464406000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Exploiting Pre-Trained Language Models for Black-Box Attack against Knowledge Graph Embeddings"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1173-5907","authenticated-orcid":false,"given":"Guangqian","family":"Yang","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2839-8693","authenticated-orcid":false,"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2795-5478","authenticated-orcid":false,"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"People\u2019s Daily Online, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6249-5315","authenticated-orcid":false,"given":"Hongtao","family":"Xie","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5739-8126","authenticated-orcid":false,"given":"Zhendong","family":"Mao","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"2069","volume-title":"Proceedings of the 2021 IEEE 37th International Conference on Data Engineering (ICDE \u201921)","author":"Banerjee Prithu","year":"2021","unstructured":"Prithu Banerjee, Lingyang Chu, Yong Zhang, Laks V. S. Lakshmanan, and Lanjun Wang. 2021. Stealthy targeted data poisoning attack on knowledge graphs. In Proceedings of the 2021 IEEE 37th International Conference on Data Engineering (ICDE \u201921). IEEE, 2069\u20132074."},{"key":"e_1_3_1_3_2","first-page":"2820","volume-title":"Proceedings of the 31st International Joint Conference on Artificial Intelligence","author":"Betz Patrick","year":"2022","unstructured":"Patrick Betz, Christian Meilicke, and Heiner Stuckenschmidt. 2022. Adversarial explanations for knowledge graph embeddings. In Proceedings of the 31st International Joint Conference on Artificial Intelligence, 2820\u20132826."},{"key":"e_1_3_1_4_2","doi-asserted-by":"crossref","first-page":"8225","DOI":"10.18653\/v1\/2021.emnlp-main.648","volume-title":"Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing","author":"Bhardwaj Peru","year":"2021","unstructured":"Peru Bhardwaj, John Kelleher, Luca Costabello, and Declan O\u2019Sullivan. 2021. Adversarial attacks on knowledge graph embeddings via instance attribution methods. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 8225\u20138239."},{"key":"e_1_3_1_5_2","first-page":"1875","volume-title":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing","volume":"1","author":"Bhardwaj Peru","year":"2021","unstructured":"Peru Bhardwaj, John Kelleher, Luca Costabello, and Declan O\u2019Sullivan. 2021. Poisoning knowledge graph embeddings via relation inference patterns. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Vol. 1 (Long Papers), 1875\u20131888."},{"key":"e_1_3_1_6_2","first-page":"2787","article-title":"Translating embeddings for modeling multi-relational data","volume":"2","author":"Bordes Antoine","year":"2013","unstructured":"Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. Advances in Neural Information Processing Systems 2 (2013), 2787\u20132795.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_7_2","unstructured":"Runjin Chen Tong Zhao Ajay Jaiswal Neil Shah and Zhangyang Wang. 2024. LLaGA: Large language and graph assistant. arXiv:2402.08170. Retrieved from https:\/\/arxiv.org\/abs\/2402.08170"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11573"},{"key":"e_1_3_1_9_2","first-page":"4171","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","volume":"1","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol. 1 (Long and Short Papers), 4171\u20134186."},{"key":"e_1_3_1_10_2","unstructured":"Ian J. Goodfellow Jonathon Shlens and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv:1412.6572. Retrieved from https:\/\/arxiv.org\/abs\/1412.6572"},{"key":"e_1_3_1_11_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Hanawa Kazuaki","year":"2021","unstructured":"Kazuaki Hanawa, Sho Yokoi, Satoshi Hara, and Kentaro Inui. 2021. Evaluation of similarity-based explanations. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.153"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"e_1_3_1_14_2","unstructured":"Yuhan Li Zhixun Li Peisong Wang Jia Li Xiangguo Sun Hong Cheng and Jeffrey Xu Yu. 2023. A survey of graph meets large language model: Progress and future directions. arXiv:2311.12399. Retrieved from https:\/\/arxiv.org\/abs\/2311.12399"},{"key":"e_1_3_1_15_2","first-page":"1","article-title":"Attention is not the only choice: Counterfactual reasoning for path-based explainable recommendation","author":"Li Yicong","year":"2024","unstructured":"Yicong Li, Xiangguo Sun, Hongxu Chen, Sixiao Zhang, Yu Yang, and Guandong Xu. 2024. Attention is not the only choice: Counterfactual reasoning for path-based explainable recommendation. IEEE Transactions on Knowledge and Data Engineering (2024), 1\u201314.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"e_1_3_1_17_2","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. arXiv:1907.11692. Retrieved from https:\/\/arxiv.org\/abs\/1907.11692"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.findings-acl.282"},{"key":"e_1_3_1_19_2","first-page":"3111","article-title":"Distributed representations of words and phrases and their compositionality","volume":"2","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems 2 (2013), 3111\u20133119.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-1202"},{"key":"e_1_3_1_23_2","first-page":"3336","volume-title":"Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL \u201919)","author":"Pezeshkpour Pouya","year":"2019","unstructured":"Pouya Pezeshkpour, Yifan Tian, and Sameer Singh. 2019. Investigating robustness and interpretability of link prediction via adversarial modifications. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL \u201919), 3336\u20133347."},{"key":"e_1_3_1_24_2","unstructured":"Alec Radford Karthik Narasimhan Tim Salimans and Ilya Sutskever. 2018. Improving language understanding by generative pre-training."},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/3424672"},{"key":"e_1_3_1_26_2","first-page":"122","volume-title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics","volume":"1","author":"Sharma Aditya","year":"2018","unstructured":"Aditya Sharma and Partha Talukdar. 2018. Towards understanding the geometry of knowledge graph embeddings. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Vol. 1 (Long Papers), 122\u2013131."},{"key":"e_1_3_1_27_2","first-page":"1","article-title":"Counter-empirical attacking based on adversarial reinforcement learning for time-relevant scoring system","author":"Sun Xiangguo","year":"2023","unstructured":"Xiangguo Sun, Hong Cheng, Hang Dong, Bo Qiao, Si Qin, and Qingwei Lin. 2023. Counter-empirical attacking based on adversarial reinforcement learning for time-relevant scoring system. IEEE Transactions on Knowledge and Data Engineering (2023), 1\u201312.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599256"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3235312"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441835"},{"issue":"9","key":"e_1_3_1_31_2","first-page":"9128","article-title":"Structure learning via meta-hyperedge for dynamic rumor detection","volume":"35","author":"Sun Xiangguo","year":"2022","unstructured":"Xiangguo Sun, Hongzhi Yin, Bo Liu, Qing Meng, Jiuxin Cao, Alexander Zhou, and Hongxu Chen. 2022. Structure learning via meta-hyperedge for dynamic rumor detection. IEEE Transactions on Knowledge and Data Engineering 35, 9 (2022), 9128\u20139139.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_32_2","unstructured":"Xiangguo Sun Jiawen Zhang Xixi Wu Hong Cheng Yun Xiong and Jia Li. 2023. Graph prompt learning: A comprehensive survey and beyond. arXiv:2311.16534. Retrieved from https:\/\/arxiv.org\/abs\/2311.16534"},{"key":"e_1_3_1_33_2","unstructured":"Yu Sun Shuohuan Wang Yukun Li Shikun Feng Xuyi Chen Han Zhang Xin Tian Danxiang Zhu Hao Tian and Hua Wu. 2019. Ernie: Enhanced representation through knowledge integration. arXiv:1904.09223. Retrieved from https:\/\/arxiv.org\/abs\/1904.09223"},{"key":"e_1_3_1_34_2","unstructured":"Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna Dumitru Erhan Ian Goodfellow and Rob Fergus. 2013. Intriguing properties of neural networks. arXiv:1312.6199. Retrieved from https:\/\/arxiv.org\/abs\/1312.6199"},{"key":"e_1_3_1_35_2","unstructured":"Jiabin Tang Yuhao Yang Wei Wei Lei Shi Lixin Su Suqi Cheng Dawei Yin and Chao Huang. 2023. Graphgpt: Graph instruction tuning for large language models. arXiv:2310.13023. Retrieved from https:\/\/arxiv.org\/abs\/2310.13023"},{"key":"e_1_3_1_36_2","first-page":"2071","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Trouillon Th\u00e9o","year":"2016","unstructured":"Th\u00e9o Trouillon, Johannes Welbl, Sebastian Riedel, \u00c9ric Gaussier, and Guillaume Bouchard. 2016. Complex embeddings for simple link prediction. In Proceedings of the International Conference on Machine Learning. PMLR, 2071\u20132080."},{"key":"e_1_3_1_37_2","first-page":"6000","article-title":"Attention is all you need","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017), 6000\u20136010.","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"e_1_3_1_38_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3533017","article-title":"Multi-concept representation learning for knowledge graph completion","volume":"17","author":"Wang Jiapu","year":"2023","unstructured":"Jiapu Wang, Boyue Wang, Junbin Gao, Yongli Hu, and Baocai Yin. 2023. Multi-concept representation learning for knowledge graph completion. ACM Transactions on Knowledge Discovery from Data 17, 1 (2023), 1\u201319.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.5555\/3620237.3620420"},{"key":"e_1_3_1_40_2","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR \u201915)","author":"Yang Bishan","year":"2015","unstructured":"Bishan Yang, Scott Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015. Embedding entities and relations for learning and inference in knowledge bases. In Proceedings of the International Conference on Learning Representations (ICLR \u201915)."},{"key":"e_1_3_1_41_2","unstructured":"Liang Yao Chengsheng Mao and Yuan Luo. 2019. KG-BERT: BERT for knowledge graph completion. arXiv:1909.03193. Retrieved from https:\/\/arxiv.org\/abs\/1909.03193"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.5555\/3367471.3367718"},{"key":"e_1_3_1_43_2","unstructured":"Sixiao Zhang Hongxu Chen Haoran Yang Xiangguo Sun Philip S. Yu and Guandong Xu. 2022. Graph masked autoencoders with transformers. arXiv:2202.08391. Retrieved from https:\/\/arxiv.org\/abs\/2202.08391"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657702"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3688850","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3688850","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:10Z","timestamp":1750291450000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3688850"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,29]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1,31]]}},"alternative-id":["10.1145\/3688850"],"URL":"https:\/\/doi.org\/10.1145\/3688850","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,29]]},"assertion":[{"value":"2024-03-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-08-04","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-11-29","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}