{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T14:10:00Z","timestamp":1767449400882,"version":"3.48.0"},"publisher-location":"Singapore","reference-count":11,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819534524"},{"type":"electronic","value":"9789819534531"}],"license":[{"start":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:00:00Z","timestamp":1760572800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:00:00Z","timestamp":1760572800000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-3453-1_35","type":"book-chapter","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T08:01:06Z","timestamp":1760515266000},"page":"451-458","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Path-Aware Framework for\u00a0Multi-hop Question Answering via\u00a0Structured Reasoning"],"prefix":"10.1007","author":[{"given":"Shihao","family":"Hu","sequence":"first","affiliation":[]},{"given":"Jiantong","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Luo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,16]]},"reference":[{"key":"35_CR1","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[J]. Adv. Neural. Inf. Process. Syst. 33, 9459\u20139474 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"35_CR2","doi-asserted-by":"crossref","unstructured":"Cuconasu, F., Trappolini, G., Siciliano, F., et al.: The power of noise: redefining retrieval for rag systems [C]. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 719-729 (2024)","DOI":"10.1145\/3626772.3657834"},{"key":"35_CR3","doi-asserted-by":"crossref","unstructured":"Mavi, V., Jangra, A., Jatowt, A.: Multi-hop question answering [J]. Found. Trends\u00ae Inf. Retrieval 17(5), 457-586 (2024)","DOI":"10.1561\/1500000102"},{"key":"35_CR4","doi-asserted-by":"crossref","unstructured":"Shao, Z., Gong, Y., Shen, Y., et al.: Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy [C]. Find. Assoc. Comput. Linguist. EMNLP 2023, 9248-9274 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.620"},{"key":"35_CR5","unstructured":"Asai, A., Wu, Z., Wang, Y., et al.: Self-rag: Learning to retrieve, generate, and critique through self-reflection[C]. The Twelfth International Conference on Learning Representations (2023)"},{"key":"35_CR6","doi-asserted-by":"crossref","unstructured":"Ammann, P.J.L., Golde, J., Akbik, A.: Question decomposition for retrieval-augmented generation [J]. arXiv preprint arXiv:2507.00355 (2025)","DOI":"10.18653\/v1\/2025.acl-srw.32"},{"key":"35_CR7","unstructured":"Chan, C.M., Xu, C., Yuan, R., et al.: RQ-RAG: learning to refine queries for retrieval augmented generation [J]. arXiv preprint arXiv:2404.00610 (2024)"},{"key":"35_CR8","unstructured":"Edge, D., Trinh, H., Cheng, N., et al.: From local to global: a graph RAG approach to query-focused summarization [J]. arXiv preprint arXiv:2404.16130 (2024)"},{"key":"35_CR9","doi-asserted-by":"crossref","unstructured":"Yang, Z., Qi, P., Zhang, S., et al.: HotpotQA: a dataset for diverse, explainable multi-hop question answering [J]. arXiv preprint arXiv:1809.09600 (2018)","DOI":"10.18653\/v1\/D18-1259"},{"key":"35_CR10","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[J]. Trans. Assoc. Comput. Linguist. 10, 539\u2013554 (2022)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"35_CR11","doi-asserted-by":"crossref","unstructured":"Ho, X., Nguyen, A.K.D., Sugawara, S., et al.: Constructing a multi-hop QA dataset for comprehensive evaluation of reasoning steps [J]. arXiv preprint arXiv:2011.01060 (2020)","DOI":"10.18653\/v1\/2020.coling-main.580"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3453-1_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T14:06:53Z","timestamp":1767449213000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3453-1_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,16]]},"ISBN":["9789819534524","9789819534531"],"references-count":11,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3453-1_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,10,16]]},"assertion":[{"value":"16 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2025.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}