{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T08:03:49Z","timestamp":1784189029210,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819234165","type":"print"},{"value":"9789819234172","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-981-92-3417-2_47","type":"book-chapter","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T07:11:10Z","timestamp":1784185870000},"page":"552-563","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DyGraph: A Dynamic Priority-Aware Framework for Multi-Hop Reasoning Over Knowledge Graph with Large Language Model"],"prefix":"10.1007","author":[{"given":"Yuhang","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingjing","family":"Fei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianwen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,17]]},"reference":[{"key":"47_CR1","first-page":"1877","volume-title":"Advances in Neural Information Processing Systems","author":"T Brown","year":"2020","unstructured":"Brown, T., Mann, B., Ryder, N., et al.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems, pp. 1877\u20131901 (2020)"},{"key":"47_CR2","doi-asserted-by":"crossref","unstructured":"Huang, L., Yu, W., Ma, W., et al.: A survey on hallucination in large language models: principles, taxonomy, challenges, and open questions. ACM Trans. Inf. Syst. 43 (2025)","DOI":"10.1145\/3703155"},{"key":"47_CR3","first-page":"9459","volume-title":"Advances in Neural Information Processing Systems","author":"P Lewis","year":"2020","unstructured":"Lewis, P., Perez, E., et al.: Retrieval-augmented generation for knowledge intensive NLP tasks. In: Advances in Neural Information Processing Systems, pp. 9459\u20139474 (2020)"},{"key":"47_CR4","doi-asserted-by":"publisher","first-page":"3580","DOI":"10.1109\/TKDE.2024.3352100","volume":"36","author":"S Pan","year":"2024","unstructured":"Pan, S., Luo, L., et al.: Unifying large language models and knowledge graphs: a roadmap. IEEE Trans. Knowl. Data Eng. 36, 3580\u20133599 (2024)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"47_CR5","doi-asserted-by":"crossref","unstructured":"Jiang, J., Zhou, K., Dong, Z., et al.: StructGPT: a general framework for large language model to reason over structured data. In: Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. pp. 9237\u20139251 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.574"},{"key":"47_CR6","volume-title":"The Twelfth International Conference on Learning Representations","author":"L Luo","year":"2024","unstructured":"Luo, L., Li, Y.-F., Haffari, G., et al.: Reasoning on graphs: faithful and interpretable large language model reasoning. In: The Twelfth International Conference on Learning Representations (2024)"},{"key":"47_CR7","volume-title":"The Twelfth International Conference on Learning Representations","author":"J Sun","year":"2024","unstructured":"Sun, J., Xu, C., Tang, L., et al.: Think-on-graph: deep and responsible reasoning of large language model on knowledge graph. In: The Twelfth International Conference on Learning Representations (2024)"},{"key":"47_CR8","first-page":"37665","volume-title":"Advances in Neural Information Processing Systems","author":"L Chen","year":"2024","unstructured":"Chen, L., Tong, P., Jin, Z., et al.: Plan-on-graph: self-correcting adaptive planning of large language model on knowledge graphs. In: Advances in Neural Information Processing Systems, pp. 37665\u201337691 (2024)"},{"key":"47_CR9","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.18653\/v1\/D13-1160","volume-title":"Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing","author":"J Berant","year":"2013","unstructured":"Berant, J., Chou, A., Frostig, R., et al.: Semantic parsing on freebase from question-answer Pairs. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1533\u20131544 (2013)"},{"key":"47_CR10","doi-asserted-by":"publisher","first-page":"8108","DOI":"10.18653\/v1\/2022.emnlp-main.555","volume-title":"Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing","author":"Y Shu","year":"2022","unstructured":"Shu, Y., Yu, Z., Li, Y., et al.: TIARA: multi-grained retrieval for robust question answering over large knowledge base. In: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 8108\u20138121 (2022)"},{"key":"47_CR11","first-page":"1718","volume-title":"Proceedings of the 29th International Conference on Computational Linguistics","author":"Y Gu","year":"2022","unstructured":"Gu, Y., Su, Y.: ArcaneQA: dynamic program induction and contextualized encoding for knowledge base question answering. In: Proceedings of the 29th International Conference on Computational Linguistics, pp. 1718\u20131731 (2022)"},{"key":"47_CR12","doi-asserted-by":"publisher","first-page":"2380","DOI":"10.18653\/v1\/D19-1242","volume-title":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","author":"H Sun","year":"2019","unstructured":"Sun, H., Bedrax-Weiss, T., Cohen, W.: PullNet: open domain question answering with iterative retrieval on knowledge bases and text. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 2380\u20132390 (2019)"},{"key":"47_CR13","doi-asserted-by":"publisher","first-page":"4498","DOI":"10.18653\/v1\/2020.acl-main.412","volume-title":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","author":"A Saxena","year":"2020","unstructured":"Saxena, A., Tripathi, A., Talukdar, P.: Improving multi-hop question answering over knowledge graphs using knowledge base embeddings. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 4498\u20134507 (2020)"},{"key":"47_CR14","first-page":"5773","volume-title":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics","author":"J Zhang","year":"2022","unstructured":"Zhang, J., Zhang, X., Yu, J., et al.: Subgraph retrieval enhanced model for multi-hop knowledge base question answering. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 5773\u20135784. Long Papers (2022)"},{"key":"47_CR15","doi-asserted-by":"publisher","first-page":"18410","DOI":"10.18653\/v1\/2024.emnlp-main.1023","volume-title":"Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing","author":"Y Xu","year":"2024","unstructured":"Xu, Y., He, S., Chen, J., et al.: Generate-on-graph: treat LLM as both agent and KG for incomplete knowledge graph question answering. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 18410\u201318430 (2024)"},{"key":"47_CR16","volume-title":"The Eleventh International Conference on Learning Representations","author":"D Yu","year":"2023","unstructured":"Yu, D., Zhang, S., Ng, P., et al.: DecAF: joint decoding of answers and logical forms for question answering over knowledge bases. In: The Eleventh International Conference on Learning Representations (2023)"},{"key":"47_CR17","first-page":"10561","volume-title":"Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics","author":"G Xiong","year":"2024","unstructured":"Xiong, G., Bao, J., Zhao, W.: Interactive-KBQA: multi-turn interactions for knowledge base question answering with large language models. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 10561\u201310582. Long Papers (2024)"},{"key":"47_CR18","doi-asserted-by":"publisher","first-page":"2447","DOI":"10.18653\/v1\/2022.findings-emnlp.181","volume-title":"Findings of the Association for Computational Linguistics: EMNLP 2022","author":"C Mavromatis","year":"2022","unstructured":"Mavromatis, C., Karypis, G.: ReaRev: adaptive reasoning for question answering over knowledge graphs. In: Findings of the Association for Computational Linguistics: EMNLP 2022, pp. 2447\u20132458 (2022)"},{"key":"47_CR19","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1145\/3437963.3441753","volume-title":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","author":"G He","year":"2021","unstructured":"He, G., Lan, Y., Jiang, J., et al.: Improving multi-hop knowledge base question answering by learning intermediate supervision signals. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 553\u2013561 (2021)"},{"key":"47_CR20","first-page":"16682","volume-title":"Findings of the Association for Computational Linguistics","author":"C Mavromatis","year":"2025","unstructured":"Mavromatis, C., Karypis, G.: GNN-RAG: graph neural retrieval for efficient large language model reasoning on knowledge graphs. In: Findings of the Association for Computational Linguistics, pp. 16682\u201316699. ACL (2025)"},{"key":"47_CR21","first-page":"24824","volume-title":"Advances in Neural Information Processing Systems","author":"J Wei","year":"2022","unstructured":"Wei, J., Wang, X., Schuurmans, D., et al.: Chain-of-thought prompting elicits reasoning in large language models. In: Advances in Neural Information Processing Systems, pp. 24824\u201324837 (2022)"},{"key":"47_CR22","volume-title":"ICLR 2025 Workshop on Foundation Models in the Wild","author":"M Li","year":"2025","unstructured":"Li, M., Miao, S., Li, P.: Simple is effective: the roles of graphs and large language models in knowledge-graph-based retrieval-augmented generation. In: ICLR 2025 Workshop on Foundation Models in the Wild (2025)"},{"key":"47_CR23","unstructured":"DeepSeek-AI, Liu, A., Feng, B., et al.: DeepSeek-V3 technical report. arXiv preprint https:\/\/arxiv.org\/abs\/2412.19437 (2025)"},{"key":"47_CR24","unstructured":"Yang, A., et al.: Qwen3 technical report. arXiv preprint https:\/\/arxiv.org\/abs\/2505.09388 (2025)"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3417-2_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T07:11:14Z","timestamp":1784185874000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3417-2_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,17]]},"ISBN":["9789819234165","9789819234172"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3417-2_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,17]]},"assertion":[{"value":"17 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}