{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T08:03:53Z","timestamp":1784189033031,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":21,"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_49","type":"book-chapter","created":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T07:12:08Z","timestamp":1784185928000},"page":"575-586","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Agent-GRAFT: Agent-Driven Dynamic Graph Repair for Multi-Hop Question Answering Over Fragmented Knowledge Graphs"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8333-3657","authenticated-orcid":false,"given":"Baosheng","family":"Yin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6027-671X","authenticated-orcid":false,"given":"Shengdong","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,17]]},"reference":[{"key":"49_CR1","doi-asserted-by":"publisher","first-page":"2369","DOI":"10.18653\/v1\/D18-1259","volume-title":"Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing","author":"Z Yang","year":"2018","unstructured":"Yang, Z., Qi, P., Zhang, S., et al.: HotpotQA: a dataset for diverse, explainable multi-hop question answering. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 2369\u20132380 (2018)"},{"key":"49_CR2","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1162\/tacl_a_00475","volume":"10","author":"H Trivedi","year":"2022","unstructured":"Trivedi, H., Balasubramanian, N., Khot, T., et al.: MuSiQue: multihop questions via single-hop question composition. Trans. Assoc. Comput. Linguist. 10, 539\u2013554 (2022)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"49_CR3","first-page":"9459","volume":"33","author":"P Lewis","year":"2020","unstructured":"Lewis, P., Perez, E., Piktus, A., et al.: Retrieval-augmented generation for knowledge-intensive NLP tasks. Adv. Neural Inf. Proces. Syst. 33, 9459\u20139474 (2020)","journal-title":"Adv. Neural Inf. Proces. Syst."},{"issue":"2","key":"49_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3777378","volume":"44","author":"B Peng","year":"2025","unstructured":"Peng, B., Zhu, Y., Liu, Y., et al.: Graph retrieval-augmented generation: a survey. ACM Trans. Inf. Syst. 44(2), 1\u201352 (2025)","journal-title":"ACM Trans. Inf. Syst."},{"key":"49_CR5","unstructured":"Edge, D., Trinh, H., Cheng, N., et al.: From local to global: a graph RAG approach to query-focused summarization. arXiv Preprint https:\/\/arxiv.org\/abs\/2404.16130. (2024)"},{"key":"49_CR6","doi-asserted-by":"publisher","first-page":"59532","DOI":"10.52202\/079017-1902","volume":"37","author":"BJ Guti\u00e9rrez","year":"2024","unstructured":"Guti\u00e9rrez, B.J., Shu, Y., Gu, Y., et al.: Hipporag: neurobiologically inspired long-term memory for large language models. Adv. Neural Inf. Proces. Syst. 37, 59532\u201359569 (2024)","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"49_CR7","first-page":"32628","volume-title":"International Conference on Learning Representations","author":"P Sarthi","year":"2024","unstructured":"Sarthi, P., Abdullah, S., Tuli, A., et al.: Raptor: recursive abstractive processing for tree-organized retrieval. In: International Conference on Learning Representations, pp. 32628\u201332649 (2024)"},{"key":"49_CR8","doi-asserted-by":"publisher","first-page":"6609","DOI":"10.18653\/v1\/2020.coling-main.580","volume-title":"Proceedings of the 28th International Conference on Computational Linguistics","author":"X Ho","year":"2020","unstructured":"Ho, X., Nguyen, A.K.D., Sugawara, S., et al.: Constructing a Multi-Hop QA dataset for comprehensive evaluation of reasoning steps. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 6609\u20136625 (2020)"},{"key":"49_CR9","volume-title":"IEEE ICCRD","author":"S Lu","year":"2026","unstructured":"Lu, S.: GRAFT-RAG: graph-refined and attention-fused retrieval-augmented generation for multi-hop reasoning with self-verified compression. In: IEEE ICCRD (2026)"},{"key":"49_CR10","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":"49_CR11","doi-asserted-by":"crossref","unstructured":"Kim, S., Hwang, S.J., Kim, J.H., et al.: ReGraphRAG: reorganizing fragmented knowledge graphs for multi-perspective retrieval-augmented generation. In: Findings of the Association for Computational Linguistics: EMNLP 2025, vol. 2025, pp. 5426\u20135443","DOI":"10.18653\/v1\/2025.findings-emnlp.290"},{"key":"49_CR12","doi-asserted-by":"publisher","first-page":"879","DOI":"10.18653\/v1\/2025.acl-long.43","volume-title":"Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","author":"MC Lee","year":"2025","unstructured":"Lee, M.C., Zhu, Q., Mavromatis, C., et al.: Hybgrag: hybrid retrieval-augmented generation on textual and relational knowledge bases. In: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 879\u2013893 (2025)"},{"key":"49_CR13","unstructured":"Jiang, J., Zhou, K., Zhao, W.X., et al.: Unikgqa: unified retrieval and reasoning for solving multi-hop question answering over knowledge graph. arXiv Preprint https:\/\/arxiv.org\/abs\/2212.00959. (2022)"},{"issue":"1","key":"49_CR14","doi-asserted-by":"publisher","first-page":"5233","DOI":"10.1038\/s41598-019-41695-z","volume":"9","author":"VA Traag","year":"2019","unstructured":"Traag, V.A., Waltman, L., Van Eck, N.J.: From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9(1), 5233 (2019)","journal-title":"Sci. Rep."},{"key":"49_CR15","unstructured":"Guo, Z., Xia, L., Yu, Y., et al.: Lightrag: simple and fast retrieval-augmented generation. arXiv Preprint https:\/\/arxiv.org\/abs\/2410.05779. 2(3) (2024)"},{"key":"49_CR16","unstructured":"Yao, S., Zhao, J., Yu, D., et al.: React: synergizing reasoning and acting in language models. arXiv Preprint https:\/\/arxiv.org\/abs\/2210.03629. (2022)"},{"key":"49_CR17","doi-asserted-by":"publisher","first-page":"8634","DOI":"10.52202\/075280-0377","volume":"36","author":"N Shinn","year":"2023","unstructured":"Shinn, N., Cassano, F., Gopinath, A., et al.: Reflexion: language agents with verbal reinforcement learning. Adv. Neural Inf. Proces. Syst. 36, 8634\u20138652 (2023)","journal-title":"Adv. Neural Inf. Proces. Syst."},{"key":"49_CR18","unstructured":"Bai, J., Bai, S., Chu, Y., et al.: Qwen technical report. arXiv Preprint https:\/\/arxiv.org\/abs\/2309.16609. (2023)"},{"key":"49_CR19","unstructured":"Lee, S., Shakir, A., Koenig, D., et al.: Open source strikes bread-new fluffy embeddings model. https:\/\/www.mixedbread.ai\/blog\/mxbai-embed-large-v1 (2024)"},{"key":"49_CR20","doi-asserted-by":"crossref","unstructured":"Chen, J., Xiao, S., Zhang, P., et al.: M3-embedding: multi-linguality, multi-functionality, multi-granularity text embeddings through self-knowledge distillation. In: Findings of the Association for Computational Linguistics: ACL 2024, vol. 2024, pp. 2318\u20132335","DOI":"10.18653\/v1\/2024.findings-acl.137"},{"key":"49_CR21","unstructured":"Grattafiori, A., Dubey, A., Jauhri, A., et al.: The Llama 3 herd of models. arXiv Preprint https:\/\/arxiv.org\/abs\/2407.21783. (2024)"}],"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_49","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T07:12:11Z","timestamp":1784185931000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3417-2_49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,17]]},"ISBN":["9789819234165","9789819234172"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3417-2_49","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"}}]}}