{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T19:15:53Z","timestamp":1784142953803,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":80,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T00:00:00Z","timestamp":1752969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSFC","award":["62372297,62202299,62302525,62303306"],"award-info":[{"award-number":["62372297,62202299,62302525,62303306"]}]},{"name":"The National Key Research and Development Program of China","award":["2023YFB3107100"],"award-info":[{"award-number":["2023YFB3107100"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7,20]]},"DOI":"10.1145\/3690624.3709187","type":"proceedings-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T18:44:43Z","timestamp":1743792283000},"page":"1996-2007","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graph"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4959-2774","authenticated-orcid":false,"given":"Yutong","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1805-0183","authenticated-orcid":false,"given":"Lixing","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0767-2307","authenticated-orcid":false,"given":"Shenghong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1316-7515","authenticated-orcid":false,"given":"Nan","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Design and Innovation, Tongji University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1065-4038","authenticated-orcid":false,"given":"Yang","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Design and Innovation, Tongji University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0009-9237","authenticated-orcid":false,"given":"Jiaxin","family":"Ding","sequence":"additional","affiliation":[{"name":"John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2211-2137","authenticated-orcid":false,"given":"Zhe","family":"Qu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha, Hunan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8629-4622","authenticated-orcid":false,"given":"Pan","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1719-1117","authenticated-orcid":false,"given":"Yang","family":"Bai","sequence":"additional","affiliation":[{"name":"Department of Automation, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,7,20]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al.","author":"Achiam Josh","year":"2023","unstructured":"Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)."},{"key":"e_1_3_2_2_2_1","volume-title":"Bring your own kg: Self-supervised program synthesis for zero-shot kgqa. arXiv preprint arXiv:2311.07850","author":"Agarwal Dhruv","year":"2023","unstructured":"Dhruv Agarwal, Rajarshi Das, Sopan Khosla, and Rashmi Gangadharaiah. 2023. Bring your own kg: Self-supervised program synthesis for zero-shot kgqa. arXiv preprint arXiv:2311.07850 (2023)."},{"key":"e_1_3_2_2_3_1","volume-title":"Promptner: Prompting for named entity recognition. arXiv preprint arXiv:2305.15444","author":"Ashok Dhananjay","year":"2023","unstructured":"Dhananjay Ashok and Zachary C Lipton. 2023. Promptner: Prompting for named entity recognition. arXiv preprint arXiv:2305.15444 (2023)."},{"key":"e_1_3_2_2_4_1","volume-title":"Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering. In The 61st Annual Meeting Of The Association For Computational Linguistics.","author":"Baek Jinheon","year":"2023","unstructured":"Jinheon Baek, Alham Fikri Aji, and Amir Saffari. 2023. Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering. In The 61st Annual Meeting Of The Association For Computational Linguistics."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-45072-3_6"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3641850"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1376616.1376746"},{"key":"e_1_3_2_2_8_1","volume-title":"Large-scale simple question answering with memory networks. arXiv preprint arXiv:1506.02075","author":"Bordes Antoine","year":"2015","unstructured":"Antoine Bordes, Nicolas Usunier, Sumit Chopra, and Jason Weston. 2015. Large-scale simple question answering with memory networks. arXiv preprint arXiv:1506.02075 (2015)."},{"key":"e_1_3_2_2_9_1","volume-title":"Translating embeddings for modeling multi-relational data. Advances in neural information processing systems","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, Vol. 26 (2013)."},{"key":"e_1_3_2_2_10_1","volume-title":"Lin (Eds.)","volume":"33","author":"Brown Tom","year":"2020","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 1877--1901. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf"},{"key":"e_1_3_2_2_11_1","volume-title":"Instruction mining: High-quality instruction data selection for large language models. arXiv preprint arXiv:2307.06290","author":"Cao Yihan","year":"2023","unstructured":"Yihan Cao, Yanbin Kang, and Lichao Sun. 2023. Instruction mining: High-quality instruction data selection for large language models. arXiv preprint arXiv:2307.06290 (2023)."},{"key":"e_1_3_2_2_12_1","volume-title":"Livio Pompianu, and Sandro Gabriele Tiddia.","author":"Carta Salvatore","year":"2023","unstructured":"Salvatore Carta, Alessandro Giuliani, Leonardo Piano, Alessandro Sebastian Podda, Livio Pompianu, and Sandro Gabriele Tiddia. 2023. Iterative zero-shot llm prompting for knowledge graph construction. arXiv preprint arXiv:2307.01128 (2023)."},{"key":"e_1_3_2_2_13_1","first-page":"1","article-title":"Palm: Scaling language modeling with pathways","volume":"24","author":"Chowdhery Aakanksha","year":"2023","unstructured":"Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2023. Palm: Scaling language modeling with pathways. Journal of Machine Learning Research, Vol. 24, 240 (2023), 1--113.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_14_1","volume-title":"Chatlaw: Open-source legal large language model with integrated external knowledge bases. arXiv preprint arXiv:2306.16092","author":"Cui Jiaxi","year":"2023","unstructured":"Jiaxi Cui, Zongjian Li, Yang Yan, Bohua Chen, and Li Yuan. 2023. Chatlaw: Open-source legal large language model with integrated external knowledge bases. arXiv preprint arXiv:2306.16092 (2023)."},{"key":"e_1_3_2_2_15_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_2_16_1","volume-title":"Proceedings, Part II 18","author":"Haussmann Steven","year":"2019","unstructured":"Steven Haussmann, Oshani Seneviratne, Yu Chen, Yarden Ne'eman, James Codella, Ching-Hua Chen, Deborah L McGuinness, and Mohammed J Zaki. 2019. FoodKG: a semantics-driven knowledge graph for food recommendation. In The Semantic Web--ISWC 2019: 18th International Semantic Web Conference, Auckland, New Zealand, October 26--30, 2019, Proceedings, Part II 18. Springer, 146--162."},{"key":"e_1_3_2_2_17_1","volume-title":"Llm-Tikg: Threat Intelligence Knowledge Graph Construction Utilizing Large Language Model. Available at SSRN 4671345","author":"Hu Yuelin","year":"2023","unstructured":"Yuelin Hu, Futai Zou, Jiajia Han, Xin Sun, and Yilei Wang. 2023. Llm-Tikg: Threat Intelligence Knowledge Graph Construction Utilizing Large Language Model. Available at SSRN 4671345 (2023)."},{"key":"e_1_3_2_2_18_1","volume-title":"Social-LLM: Modeling User Behavior at Scale using Language Models and Social Network Data. arXiv preprint arXiv:2401.00893","author":"Jiang Julie","year":"2023","unstructured":"Julie Jiang and Emilio Ferrara. 2023. Social-LLM: Modeling User Behavior at Scale using Language Models and Social Network Data. arXiv preprint arXiv:2401.00893 (2023)."},{"key":"e_1_3_2_2_19_1","unstructured":"Xinke Jiang Ruizhe Zhang Yongxin Xu Rihong Qiu Yue Fang Zhiyuan Wang Jinyi Tang Hongxin Ding Xu Chu Junfeng Zhao et al. 2023. Think and Retrieval: A Hypothesis Knowledge Graph Enhanced Medical Large Language Models. arXiv preprint arXiv:2312.15883 (2023)."},{"key":"e_1_3_2_2_20_1","volume-title":"Efficient Knowledge Infusion via KG-LLM Alignment. arXiv preprint arXiv:2406.03746","author":"Jiang Zhouyu","year":"2024","unstructured":"Zhouyu Jiang, Ling Zhong, Mengshu Sun, Jun Xu, Rui Sun, Hui Cai, Shuhan Luo, and Zhiqiang Zhang. 2024. Efficient Knowledge Infusion via KG-LLM Alignment. arXiv preprint arXiv:2406.03746 (2024)."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1259"},{"key":"e_1_3_2_2_22_1","first-page":"9459","article-title":"Retrieval-augmented generation for knowledge-intensive nlp tasks","volume":"33","author":"Lewis Patrick","year":"2020","unstructured":"Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich K\u00fcttler, Mike Lewis, Wen-tau Yih, Tim Rockt\u00e4schel, et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, Vol. 33 (2020), 9459--9474.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_23_1","volume-title":"Richard Yi Da Xu, and Bailing Wang","author":"Li Haotian","year":"2023","unstructured":"Haotian Li, Lingzhi Wang, Yuliang Wei, Richard Yi Da Xu, and Bailing Wang. 2023b. KERMIT: Knowledge Graph Completion of Enhanced Relation Modeling with Inverse Transformation. arXiv preprint arXiv:2309.14770 (2023)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.40895"},{"key":"e_1_3_2_2_25_1","unstructured":"Yong Lin Lu Tan Hangyu Lin Zeming Zheng Renjie Pi Jipeng Zhang Shizhe Diao Haoxiang Wang Han Zhao Yuan Yao et al. 2023. Speciality vs generality: An empirical study on catastrophic forgetting in fine-tuning foundation models. arXiv preprint arXiv:2309.06256 (2023)."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657722"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i03.5681"},{"key":"e_1_3_2_2_28_1","volume-title":"International Workshop on Machine Learning in Medical Imaging. Springer, 464--473","author":"Liu Zhengliang","year":"2023","unstructured":"Zhengliang Liu, Aoxiao Zhong, Yiwei Li, Longtao Yang, Chao Ju, Zihao Wu, Chong Ma, Peng Shu, Cheng Chen, Sekeun Kim, et al. 2023. Tailoring large language models to radiology: A preliminary approach to llm adaptation for a highly specialized domain. In International Workshop on Machine Learning in Medical Imaging. Springer, 464--473."},{"key":"e_1_3_2_2_29_1","volume-title":"Reasoning on graphs: Faithful and interpretable large language model reasoning. arXiv preprint arXiv:2310.01061","author":"Luo Linhao","year":"2023","unstructured":"Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, and Shirui Pan. 2023. Reasoning on graphs: Faithful and interpretable large language model reasoning. arXiv preprint arXiv:2310.01061 (2023)."},{"key":"e_1_3_2_2_30_1","volume-title":"When not to trust language models: Investigating effectiveness of parametric and non-parametric memories. arXiv preprint arXiv:2212.10511","author":"Mallen Alex","year":"2022","unstructured":"Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2022. When not to trust language models: Investigating effectiveness of parametric and non-parametric memories. arXiv preprint arXiv:2212.10511 (2022)."},{"key":"e_1_3_2_2_31_1","volume-title":"KRAGEN: a knowledge Graph-Enhanced RAG framework for biomedical problem solving using large language models. Bioinformatics","author":"Matsumoto Nicholas","year":"2024","unstructured":"Nicholas Matsumoto, Jay Moran, Hyunjun Choi, Miguel E Hernandez, Mythreye Venkatesan, Paul Wang, and Jason H Moore. 2024. KRAGEN: a knowledge Graph-Enhanced RAG framework for biomedical problem solving using large language models. Bioinformatics (2024), btae353."},{"key":"e_1_3_2_2_32_1","volume-title":"Working conference on Artificial Intelligence Development for a Resilient and Sustainable Tomorrow. Springer Fachmedien Wiesbaden Wiesbaden, 103--115","author":"Meyer Lars-Peter","year":"2023","unstructured":"Lars-Peter Meyer, Claus Stadler, Johannes Frey, Norman Radtke, Kurt Junghanns, Roy Meissner, Gordian Dziwis, Kirill Bulert, and Michael Martin. 2023. Llm-assisted knowledge graph engineering: Experiments with chatgpt. In Working conference on Artificial Intelligence Development for a Resilient and Sustainable Tomorrow. Springer Fachmedien Wiesbaden Wiesbaden, 103--115."},{"key":"e_1_3_2_2_33_1","volume-title":"Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozi\u00e8re, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, et al.","author":"Mialon Gr\u00e9goire","year":"2023","unstructured":"Gr\u00e9goire Mialon, Roberto Dess`i, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozi\u00e8re, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, et al. 2023. Augmented language models: a survey. arXiv preprint arXiv:2302.07842 (2023)."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-47243-5_14"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.5121\/ijnlc.2012.1402"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645616"},{"key":"e_1_3_2_2_37_1","unstructured":"OpenAI. [n. d.]. Chat Completions Guide. https:\/\/platform.openai.com\/docs\/guides\/chat-completions. Accessed: 2024-08-06."},{"key":"e_1_3_2_2_38_1","unstructured":"Long Ouyang Jeffrey Wu Xu Jiang Diogo Almeida Carroll Wainwright Pamela Mishkin Chong Zhang Sandhini Agarwal Katarina Slama Alex Ray et al. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems Vol. 35 (2022) 27730--27744."},{"key":"e_1_3_2_2_39_1","volume-title":"Proceedings of the Conference on Health, Inference, and Learning (Proceedings of Machine Learning Research","volume":"260","author":"Pal Ankit","year":"2022","unstructured":"Ankit Pal, Logesh Kumar Umapathi, and Malaikannan Sankarasubbu. 2022. MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering. In Proceedings of the Conference on Health, Inference, and Learning (Proceedings of Machine Learning Research, Vol. 174), Gerardo Flores, George H Chen, Tom Pollard, Joyce C Ho, and Tristan Naumann (Eds.). PMLR, 248--260."},{"key":"e_1_3_2_2_40_1","unstructured":"Jeff Z Pan Simon Razniewski Jan-Christoph Kalo Sneha Singhania Jiaoyan Chen Stefan Dietze Hajira Jabeen Janna Omeliyanenko Wen Zhang Matteo Lissandrini et al. 2023. Large language models and knowledge graphs: Opportunities and challenges. arXiv preprint arXiv:2308.06374 (2023)."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.03.431"},{"key":"e_1_3_2_2_42_1","unstructured":"Baolin Peng Michel Galley Pengcheng He Hao Cheng Yujia Xie Yu Hu Qiuyuan Huang Lars Liden Zhou Yu Weizhu Chen et al. 2023. Check your facts and try again: Improving large language models with external knowledge and automated feedback. arXiv preprint arXiv:2302.12813 (2023)."},{"key":"e_1_3_2_2_43_1","volume-title":"International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management. Springer, 149--174","author":"Regino Andr\u00e9 Gomes","year":"2022","unstructured":"Andr\u00e9 Gomes Regino, Rodrigo Oliveira Caus, Victor Hochgreb, and Julio Cesar dos Reis. 2022. From Natural Language Texts to RDF Triples: A Novel Approach to Generating e-Commerce Knowledge Graphs. In International Joint Conference on Knowledge Discovery, Knowledge Engineering, and Knowledge Management. Springer, 149--174."},{"key":"e_1_3_2_2_44_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Sadeghian Ali","year":"2019","unstructured":"Ali Sadeghian, Mohammadreza Armandpour, Patrick Ding, and Daisy Zhe Wang. 2019. Drum: End-to-end differentiable rule mining on knowledge graphs. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00799-022-00329-y"},{"key":"e_1_3_2_2_46_1","volume-title":"LLM Driven Web Profile Extraction for Identical Names. In Companion Proceedings of the ACM on Web Conference","author":"Sancheti Prateek","year":"2024","unstructured":"Prateek Sancheti, Kamalakar Karlapalem, and Kavita Vemuri. 2024. LLM Driven Web Profile Extraction for Identical Names. In Companion Proceedings of the ACM on Web Conference 2024. 1616--1625."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.412"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-5322"},{"key":"e_1_3_2_2_49_1","volume-title":"Retrieval augmentation reduces hallucination in conversation. arXiv preprint arXiv:2104.07567","author":"Shuster Kurt","year":"2021","unstructured":"Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. 2021. Retrieval augmentation reduces hallucination in conversation. arXiv preprint arXiv:2104.07567 (2021)."},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"crossref","unstructured":"Karthik Soman Peter W Rose John H Morris Rabia E Akbas Brett Smith Braian Peetoom Catalina Villouta-Reyes Gabriel Cerono Yongmei Shi Angela Rizk-Jackson et al. 2023. Biomedical knowledge graph-enhanced prompt generation for large language models. arXiv preprint arXiv:2311.17330 (2023).","DOI":"10.1093\/bioinformatics\/btae560"},{"key":"e_1_3_2_2_51_1","volume-title":"LawLuo: A Chinese Law Firm Co-run by LLM Agents. arXiv preprint arXiv:2407.16252","author":"Sun Jingyun","year":"2024","unstructured":"Jingyun Sun, Chengxiao Dai, Zhongze Luo, Yangbo Chang, and Yang Li. 2024. LawLuo: A Chinese Law Firm Co-run by LLM Agents. arXiv preprint arXiv:2407.16252 (2024)."},{"key":"e_1_3_2_2_52_1","volume-title":"Think-on-graph: Deep and responsible reasoning of large language model with knowledge graph. arXiv preprint arXiv:2307.07697","author":"Sun Jiashuo","year":"2023","unstructured":"Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Heung-Yeung Shum, and Jian Guo. 2023. Think-on-graph: Deep and responsible reasoning of large language model with knowledge graph. arXiv preprint arXiv:2307.07697 (2023)."},{"key":"e_1_3_2_2_53_1","volume-title":"Colake: Contextualized language and knowledge embedding. arXiv preprint arXiv:2010.00309","author":"Sun Tianxiang","year":"2020","unstructured":"Tianxiang Sun, Yunfan Shao, Xipeng Qiu, Qipeng Guo, Yaru Hu, Xuanjing Huang, and Zheng Zhang. 2020. Colake: Contextualized language and knowledge embedding. arXiv preprint arXiv:2010.00309 (2020)."},{"key":"e_1_3_2_2_54_1","volume-title":"Leveraging llms in scholarly knowledge graph question answering. arXiv preprint arXiv:2311.09841","author":"Taffa Tilahun Abedissa","year":"2023","unstructured":"Tilahun Abedissa Taffa and Ricardo Usbeck. 2023. Leveraging llms in scholarly knowledge graph question answering. arXiv preprint arXiv:2311.09841 (2023)."},{"key":"e_1_3_2_2_55_1","unstructured":"Gemini Team Rohan Anil Sebastian Borgeaud Yonghui Wu Jean-Baptiste Alayrac Jiahui Yu Radu Soricut Johan Schalkwyk Andrew M Dai Anja Hauth et al. 2023. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805 (2023)."},{"key":"e_1_3_2_2_56_1","volume-title":"Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting.","author":"Thirunavukarasu Arun James","year":"2023","unstructured":"Arun James Thirunavukarasu, Darren Shu Jeng Ting, Kabilan Elangovan, Laura Gutierrez, Ting Fang Tan, and Daniel Shu Wei Ting. 2023. Large language models in medicine. Nature medicine, Vol. 29, 8 (2023), 1930--1940."},{"key":"e_1_3_2_2_57_1","volume-title":"Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al.","author":"Thoppilan Romal","year":"2022","unstructured":"Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al. 2022. Lamda: Language models for dialog applications. arXiv preprint arXiv:2201.08239 (2022)."},{"key":"e_1_3_2_2_58_1","volume-title":"Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971","author":"Touvron Hugo","year":"2023","unstructured":"Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth\u00e9e Lacroix, Baptiste Rozi\u00e8re, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)."},{"key":"e_1_3_2_2_59_1","volume-title":"International conference on machine learning. PMLR","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 International conference on machine learning. PMLR, 2071--2080."},{"key":"e_1_3_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/2629489"},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450043"},{"key":"e_1_3_2_2_62_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.295"},{"key":"e_1_3_2_2_63_1","volume-title":"Gpt-ner: Named entity recognition via large language models. arXiv preprint arXiv:2304.10428","author":"Wang Shuhe","year":"2023","unstructured":"Shuhe Wang, Xiaofei Sun, Xiaoya Li, Rongbin Ouyang, Fei Wu, Tianwei Zhang, Jiwei Li, and Guoyin Wang. 2023. Gpt-ner: Named entity recognition via large language models. arXiv preprint arXiv:2304.10428 (2023)."},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33017208"},{"key":"e_1_3_2_2_65_1","volume-title":"Denny Zhou, et al.","author":"Wei Jason","year":"2022","unstructured":"Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, Vol. 35 (2022), 24824--24837."},{"key":"e_1_3_2_2_66_1","volume-title":"KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion. arXiv preprint arXiv:2402.02389","author":"Wei Yanbin","year":"2024","unstructured":"Yanbin Wei, Qiushi Huang, James T Kwok, and Yu Zhang. 2024. KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion. arXiv preprint arXiv:2402.02389 (2024)."},{"key":"e_1_3_2_2_67_1","volume-title":"Crowdsourcing Multiple Choice Science Questions. ArXiv","author":"Welbl Johannes","year":"2017","unstructured":"Johannes Welbl, Nelson F. Liu, and Matt Gardner. 2017. Crowdsourcing Multiple Choice Science Questions. ArXiv, Vol. abs\/1707.06209 (2017). https:\/\/api.semanticscholar.org\/CorpusID:1553193"},{"key":"e_1_3_2_2_68_1","volume-title":"Mindmap: Knowledge graph prompting sparks graph of thoughts in large language models. arXiv preprint arXiv:2308.09729","author":"Wen Yilin","year":"2023","unstructured":"Yilin Wen, Zifeng Wang, and Jimeng Sun. 2023. Mindmap: Knowledge graph prompting sparks graph of thoughts in large language models. arXiv preprint arXiv:2308.09729 (2023)."},{"key":"e_1_3_2_2_69_1","volume-title":"Retrieve-rewrite-answer: A KG-to-text enhanced LLMS framework for knowledge graph question answering. arXiv preprint arXiv:2309.11206","author":"Wu Yike","year":"2023","unstructured":"Yike Wu, Nan Hu, Guilin Qi, Sheng Bi, Jie Ren, Anhuan Xie, and Wei Song. 2023. Retrieve-rewrite-answer: A KG-to-text enhanced LLMS framework for knowledge graph question answering. arXiv preprint arXiv:2309.11206 (2023)."},{"key":"e_1_3_2_2_70_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-demos.15"},{"key":"e_1_3_2_2_71_1","volume-title":"Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I Wang, et al.","author":"Xie Tianbao","year":"2022","unstructured":"Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I Wang, et al. 2022. Unifiedskg: Unifying and multi-tasking structured knowledge grounding with text-to-text language models. arXiv preprint arXiv:2201.05966 (2022)."},{"key":"e_1_3_2_2_72_1","volume-title":"TWEETQA: A social media focused question answering dataset. arXiv preprint arXiv:1907.06292","author":"Xiong Wenhan","year":"2019","unstructured":"Wenhan Xiong, Jiawei Wu, Hong Wang, Vivek Kulkarni, Mo Yu, Shiyu Chang, Xiaoxiao Guo, and William Yang Wang. 2019. TWEETQA: A social media focused question answering dataset. arXiv preprint arXiv:1907.06292 (2019)."},{"key":"e_1_3_2_2_73_1","volume-title":"Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering. arXiv preprint arXiv:2404.14741","author":"Xu Yao","year":"2024","unstructured":"Yao Xu, Shizhu He, Jiabei Chen, Zihao Wang, Yangqiu Song, Hanghang Tong, Kang Liu, and Jun Zhao. 2024. Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering. arXiv preprint arXiv:2404.14741 (2024)."},{"key":"e_1_3_2_2_74_1","volume-title":"Differentiable learning of logical rules for knowledge base reasoning. Advances in neural information processing systems","author":"Yang Fan","year":"2017","unstructured":"Fan Yang, Zhilin Yang, and William W Cohen. 2017. Differentiable learning of logical rules for knowledge base reasoning. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_75_1","volume-title":"Fine-Tuning Medical Language Models for Enhanced Long-Contextual Understanding and Domain Expertise. arXiv preprint arXiv:2407.11536","author":"Yang Qimin","year":"2024","unstructured":"Qimin Yang, Rongsheng Wang, Jiexin Chen, Runqi Su, and Tao Tan. 2024. Fine-Tuning Medical Language Models for Enhanced Long-Contextual Understanding and Domain Expertise. arXiv preprint arXiv:2407.11536 (2024)."},{"key":"e_1_3_2_2_76_1","volume-title":"KG-BERT: BERT for knowledge graph completion. arXiv preprint arXiv:1909.03193","author":"Yao Liang","year":"2019","unstructured":"Liang Yao, Chengsheng Mao, and Yuan Luo. 2019. KG-BERT: BERT for knowledge graph completion. arXiv preprint arXiv:1909.03193 (2019)."},{"key":"e_1_3_2_2_77_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Yao Shunyu","year":"2024","unstructured":"Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2024. Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_2_78_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.emnlp-main.342"},{"key":"e_1_3_2_2_79_1","volume-title":"Bertscore: Evaluating text generation with bert. arXiv preprint arXiv:1904.09675","author":"Zhang Tianyi","year":"2019","unstructured":"Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. arXiv preprint arXiv:1904.09675 (2019)."},{"key":"e_1_3_2_2_80_1","doi-asserted-by":"publisher","DOI":"10.3115\/1073083.1073163"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690624.3709187","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3690624.3709187","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T15:47:09Z","timestamp":1755359229000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690624.3709187"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,20]]},"references-count":80,"alternative-id":["10.1145\/3690624.3709187","10.1145\/3690624"],"URL":"https:\/\/doi.org\/10.1145\/3690624.3709187","relation":{},"subject":[],"published":{"date-parts":[[2025,7,20]]},"assertion":[{"value":"2025-07-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}