{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T13:09:48Z","timestamp":1778332188300,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":29,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819203772","type":"print"},{"value":"9789819203789","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-981-92-0378-9_20","type":"book-chapter","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T12:20:59Z","timestamp":1778329259000},"page":"324-339","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Decomposing Complexity: A Difficulty-Aware Multi-agent Framework for\u00a0Open-Domain Knowledge Graph Construction"],"prefix":"10.1007","author":[{"given":"Haoyang","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,10]]},"reference":[{"key":"20_CR1","doi-asserted-by":"crossref","unstructured":"Angeli, G., Premkumar, M.J., Manning, C.D.: Leveraging linguistic structure for open domain information extraction. In: ACL, pp. 344\u2013354 (2015)","DOI":"10.3115\/v1\/P15-1034"},{"key":"20_CR2","unstructured":"Bai, J., et al.: AutoSchemaKG: autonomous knowledge graph construction through dynamic schema induction from web-scale corpora. arXiv preprint arXiv:2505.23628 (2025)"},{"key":"20_CR3","unstructured":"Cao, B., Cai, D., Zhang, Z., Zou, Y., Lam, W.: On the worst prompt performance of large language models. In: NeurIPS (2024)"},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Chen, H., Shen, X., Lv, Q., Wang, J., Ni, X., Ye, J.: SAC-KG: exploiting large language models as skilled automatic constructors for domain knowledge graphs. In: ACL, pp. 4345\u20134360 (2024)","DOI":"10.18653\/v1\/2024.acl-long.238"},{"key":"20_CR5","unstructured":"Dekoninck, J., Baader, M., Vechev, M.: A unified approach to routing and cascading for LLMs. arXiv preprint arXiv:2410.10347 (2024)"},{"key":"20_CR6","unstructured":"Douze, M., et al.: The FAISS library. arXiv preprint arXiv:2401.08281 (2024)"},{"key":"20_CR7","unstructured":"Edge, D., et al.: From local to global: a graph RAG approach to query-focused summarization. arXiv preprint arXiv:2404.16130 (2024)"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Fabbri, A.R., Li, I., She, T., Li, S., Radev, D.R.: Multi-news: a large-scale multi-document summarization dataset and abstractive hierarchical model. In: ACL, pp. 1074\u20131084 (2019)","DOI":"10.18653\/v1\/P19-1102"},{"key":"20_CR9","unstructured":"Hsieh, C.P., et al.: RULER: what\u2019s the real context size of your long-context language models? arXiv preprint arXiv:2404.06654 (2024)"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Kolluru, K., Adlakha, V., Aggarwal, S., Mausam, Chakrabarti, S.: OpenIE6: iterative grid labeling and coordination analysis for open information extraction. In: EMNLP, pp. 3748\u20133761 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.306"},{"key":"20_CR11","doi-asserted-by":"crossref","unstructured":"Liu, N.F., et al.: Lost in the middle: how language models use long contexts. Trans. Assoc. Computat. Linguist. 12, 157\u2013173 (2024)","DOI":"10.1162\/tacl_a_00638"},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Loukas, L., Fergadiotis, M., Androutsopoulos, I., Malakasiotis, P.: EDGAR-CORPUS: billions of tokens make the world go round. In: Proceedings of the Workshop on Economics and Natural Language Processing, pp. 13\u201318 (2021)","DOI":"10.18653\/v1\/2021.econlp-1.2"},{"key":"20_CR13","unstructured":"Lu, Y., Wu, W., Zhao, X., Peng, R., Wang, J.: KARMA: leveraging multi-agent LLMs for automated knowledge graph enrichment. arXiv preprint arXiv:2502.06472 (2025)"},{"key":"20_CR14","unstructured":"Madaan, A., et al.: Self-refine: iterative refinement with self-feedback. In: NeurIPS (2023)"},{"key":"20_CR15","unstructured":"Mo, B., et al.: KGGen: extracting knowledge graphs from plain text with language models. In: NeurIPS (2025)"},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Nye, B., et al.: A corpus with multi-level annotations of patients, interventions and outcomes to support language processing for medical literature. In: ACL, pp. 197\u2013207 (2018)","DOI":"10.18653\/v1\/P18-1019"},{"key":"20_CR17","unstructured":"Qwen: Yang, A., et al.: Qwen2.5 technical report. arXiv preprint arXiv:2412.15115 (2024)"},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. In: EMNLP, pp. 3982\u20133992 (2019)","DOI":"10.18653\/v1\/D19-1410"},{"key":"20_CR19","doi-asserted-by":"crossref","unstructured":"Renze, M., Guven, E.: The effect of sampling temperature on problem solving in large language models. In: EMNLP, pp. 7346\u20137356 (2024)","DOI":"10.18653\/v1\/2024.findings-emnlp.432"},{"key":"20_CR20","unstructured":"Su, J., et al.: Difficulty-aware agent orchestration in LLM-powered workflows. arXiv preprint arXiv:2509.11079 (2025)"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Tambon, F., Nikanjam, A., Zid, C., Khomh, F., Antoniol, G.: TaskEval: assessing difficulty of code generation tasks for large language models. arXiv preprint arXiv:2407.21227 (2024)","DOI":"10.1145\/3773285"},{"key":"20_CR22","unstructured":"Tong, Y., Zhang, X., Wang, R., et\u00a0al.: DART-math: difficulty-aware rejection tuning for mathematical problem-solving. In: NeurIPS (2024)"},{"key":"20_CR23","unstructured":"Touchent, R., Godey, N., de\u00a0la Clergerie, E.: Biomed-enriched: a biomedical dataset enriched with LLMs for pretraining and extracting rare and hidden content. arXiv preprint arXiv:2506.20331 (2025)"},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Wang, C., Liu, X., Chen, Z., Hong, H., Tang, J., Song, D.: Zero-shot information extraction as a unified text-to-triple translation. In: EMNLP, pp. 1225\u20131238 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.94"},{"key":"20_CR25","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Factuality of large language models: a survey. In: EMNLP, pp. 19519\u201319529 (2024)","DOI":"10.18653\/v1\/2024.emnlp-main.1088"},{"key":"20_CR26","doi-asserted-by":"crossref","unstructured":"Xu, D., et al.: Large language models for generative information extraction: a survey. Front. Comput. Sci. (2024)","DOI":"10.1007\/s11704-024-40555-y"},{"key":"20_CR27","unstructured":"Yang, A., et al.: Qwen3 technical report. arXiv preprint arXiv:2505.09388 (2025)"},{"key":"20_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, B., Soh, H.: Extract, define, canonicalize: an LLM-based framework for knowledge graph construction. In: EMNLP, pp. 9820\u20139836 (2024)","DOI":"10.18653\/v1\/2024.emnlp-main.548"},{"key":"20_CR29","doi-asserted-by":"crossref","unstructured":"Zhuo, J., Zhang, S., Fang, X., Duan, H., Lin, D., Chen, K.: ProSA: assessing and understanding the prompt sensitivity of LLMs. In: EMNLP, pp. 1950\u20131976 (2024)","DOI":"10.18653\/v1\/2024.findings-emnlp.108"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-0378-9_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T12:21:08Z","timestamp":1778329268000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-0378-9_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819203772","9789819203789"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-0378-9_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"10 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jeju","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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 April 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 April 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dasfaa2026.github.io\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}