{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T14:30:52Z","timestamp":1787495452904,"version":"build-2736575974"},"publisher-location":"Singapore","reference-count":35,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819248049","type":"print"},{"value":"9789819248056","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"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\/978-981-92-4805-6_14","type":"book-chapter","created":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T13:46:25Z","timestamp":1787492785000},"page":"203-218","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CGR: A Budget-Aware Closed-Loop Framework for\u00a0Efficient Knowledge Graph Question Answering"],"prefix":"10.1007","author":[{"given":"Yapeng","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jibin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijuan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Ao, T., et al.: Lightprof: a lightweight reasoning framework for large language model on knowledge graph. In: AAAI, pp. 23424\u201323432 (2025)","DOI":"10.1609\/aaai.v39i22.34510"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Baek, J., Aji, A.F., Saffari, A.: Knowledge-augmented language model prompting for zero-shot knowledge graph question answering. In: NLRSE, pp, 78\u2013106 (2023)","DOI":"10.18653\/v1\/2023.nlrse-1.7"},{"key":"14_CR3","unstructured":"Bai, J., et al.: Qwen technical report. arXiv preprint arXiv:2309.16609 (2023)"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Besta, M., et al.: Graph of thoughts: solving elaborate problems with large language models. In: AAAI, pp. 17682\u201317690 (2024)","DOI":"10.1609\/aaai.v38i16.29720"},{"key":"14_CR5","doi-asserted-by":"crossref","unstructured":"Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: SIGMOD, pp. 1247\u20131250 (2008)","DOI":"10.1145\/1376616.1376746"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Chen, L., Tong, P., Jin, Z., Sun, Y., Ye, J., Xiong, H.: Plan-on-graph: self-correcting adaptive planning of large language model on knowledge graphs. In: NeurIPS, pp, 37665\u201337691 (2024)","DOI":"10.52202\/079017-1189"},{"key":"14_CR7","doi-asserted-by":"crossref","unstructured":"Cheng, D., et al.: Uprise: universal prompt retrieval for improving zero-shot evaluation. In: EMNLP, pp. 12318\u201312337 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.758"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Gu, Y., et al.: Beyond IID: three levels of generalization for question answering on knowledge bases. In: WWW, pp. 3477\u20133488 (2021)","DOI":"10.1145\/3442381.3449992"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"He, X., et al.: G-retriever: retrieval-augmented generation for textual graph understanding and question answering. In: NeurIPS, pp. 132876\u2013132907 (2024)","DOI":"10.52202\/079017-4224"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Honnibal, M., Johnson, M.: An improved non-monotonic transition system for dependency parsing. In: EMNLP, pp. 1373\u20131378 (2015)","DOI":"10.18653\/v1\/D15-1162"},{"key":"14_CR11","doi-asserted-by":"crossref","unstructured":"Jiang, J., Zhou, K., Dong, Z., Ye, K., Zhao, X., Wen, J.R.: Structgpt: a general framework for large language model to reason over structured data. In: EMNLP, pp. 9237\u20139251 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.574"},{"key":"14_CR12","unstructured":"Kahneman, D.: Thinking, Fast and Slow. Macmillan (2011)"},{"key":"14_CR13","unstructured":"Khot, T., et al.: Decomposed prompting: a modular approach for solving complex tasks. In: ICLR (2023)"},{"key":"14_CR14","unstructured":"Lewis, P., et al.: Retrieval-augmented generation for knowledge-intensive nlp tasks. In: NeurIPS, pp. 9459\u20139474 (2020)"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Li, X., Zhou, Y., Dou, Z.: Unigen: a unified generative framework for retrieval and question answering with large language models. In: AAAI, pp. 8688\u20138696 (2024)","DOI":"10.1609\/aaai.v38i8.28714"},{"key":"14_CR16","unstructured":"Li, X., et al.: Chain-of-knowledge: grounding large language models via dynamic knowledge adapting over heterogeneous sources. In: ICLR, pp. 9565\u20139587 (2024)"},{"key":"14_CR17","doi-asserted-by":"crossref","unstructured":"Lin, S.C., et al.: How to train your dragon: diverse augmentation towards generalizable dense retrieval. In: Findings of ACL: EMNLP, pp. 6385\u20136400 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.423"},{"key":"14_CR18","unstructured":"Liu, A., et al.: Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437 (2024)"},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Luo, L., Ju, J., Xiong, B., Li, Y.F., Haffari, G., Pan, S.: Chatrule: mining logical rules with large language models for knowledge graph reasoning. In: PAKDD, pp. 314\u2013325 (2025)","DOI":"10.1007\/978-981-96-8173-0_25"},{"key":"14_CR20","unstructured":"Luo, L., Li, Y.F., Haffari, G., Pan, S.: Reasoning on graphs: faithful and interpretable large language model reasoning. In: ICLR, pp. 14400\u201314423 (2024)"},{"key":"14_CR21","doi-asserted-by":"publisher","first-page":"3580","DOI":"10.1109\/TKDE.2024.3352100","volume":"7","author":"S Pan","year":"2024","unstructured":"Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., Wu, X.: Unifying large language models and knowledge graphs: a roadmap. IEEE Trans. Knowl. Data Eng. 7, 3580\u20133599 (2024)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"14_CR22","doi-asserted-by":"crossref","unstructured":"Petroni, F., Ret al.: Language models as knowledge bases? In: EMNLP-IJCNLP, pp. 2463\u20132473 (2019)","DOI":"10.18653\/v1\/D19-1250"},{"key":"14_CR23","first-page":"68539","volume":"36","author":"T Schick","year":"2023","unstructured":"Schick, T., et al.: Toolformer: language models can teach themselves to use tools. NeurIPS 36, 68539\u201368551 (2023)","journal-title":"NeurIPS"},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"Shi, W., et al.: Replug: retrieval-augmented black-box language models. In: NAACL-HLT, pp. 8371\u20138384 (2024)","DOI":"10.18653\/v1\/2024.naacl-long.463"},{"key":"14_CR25","unstructured":"Sun, J., et al.: Think-on-graph: deep and responsible reasoning of large language model on knowledge graph. In: ICLR, pp. 3868\u20133898 (2024)"},{"key":"14_CR26","doi-asserted-by":"crossref","unstructured":"Talmor, A., Berant, J.: The web as a knowledge-base for answering complex questions. In: NAACL-HLT, pp. 641\u2013651 (2018)","DOI":"10.18653\/v1\/N18-1059"},{"key":"14_CR27","doi-asserted-by":"crossref","unstructured":"Wang, Q., et al.: Paperrobot: incremental draft generation of scientific ideas. In: ACL, pp. 1980\u20131991 (2019)","DOI":"10.18653\/v1\/P19-1191"},{"key":"14_CR28","doi-asserted-by":"crossref","unstructured":"Wang, S., Sui, Y., Wang, C., Xiong, H.: Unleashing the power of knowledge graph for recommendation via invariant learning. In: WWW, pp. 3745\u20133755 (2024)","DOI":"10.1145\/3589334.3645576"},{"key":"14_CR29","unstructured":"Wang, S., Lin, J., Guo, X., Shun, J., Li, J., Zhu, Y.: Reasoning of large language models over knowledge graphs with super-relations. In: ICLR, pp. 4208\u20134226 (2025)"},{"key":"14_CR30","doi-asserted-by":"crossref","unstructured":"Wei, J., et al.: Chain-of-thought prompting elicits reasoning in large language models. In: NeurIPS, pp, 24824\u201324837 (2022)","DOI":"10.52202\/068431-1800"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Wen, Y., Wang, Z., Sun, J.: Mindmap: knowledge graph prompting sparks deep reasoning in large language models. In: ACL, pp. 10370\u201310388 (2024)","DOI":"10.18653\/v1\/2024.acl-long.558"},{"key":"14_CR32","doi-asserted-by":"crossref","unstructured":"Xiong, W., Hoang, T., Wang, W.Y.: Deeppath: a reinforcement learning method for knowledge graph reasoning. In: EMNLP, pp, 564\u2013573 (2017)","DOI":"10.18653\/v1\/D17-1060"},{"key":"14_CR33","doi-asserted-by":"crossref","unstructured":"Yih, W.T., Richardson, M., Meek, C., Chang, M.W., Suh, J.: The value of semantic parse labeling for knowledge base question answering. In: ACL, pp. 201\u2013206 (2016)","DOI":"10.18653\/v1\/P16-2033"},{"key":"14_CR34","unstructured":"Yoran, O., Wolfson, T., Ram, O., Berant, J.: Making retrieval-augmented language models robust to irrelevant context. In: ICLR, pp. 29862\u201329883 (2024)"},{"key":"14_CR35","unstructured":"Zhang, H., et al.: Poolingformer: long document modeling with pooling attention. In: ICML, pp. 12437\u201312446 (2021)"}],"container-title":["Lecture Notes in Computer Science","Advanced Parallel Processing Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-4805-6_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T13:46:29Z","timestamp":1787492789000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-4805-6_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,24]]},"ISBN":["9789819248049","9789819248056"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-4805-6_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,24]]},"assertion":[{"value":"24 August 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"Artificial intelligence tools, if used, were used only for language organization and figure\/table polishing, and did not participate in the generation of the core ideas or technical content.","order":2,"name":"Ethics","label":"AI Usage Statement.","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"APPT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Advanced Parallel Processing Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brussels","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belgium","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":"27 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"appt2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.appt-conference.com\/2026","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}