{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T13:34:04Z","timestamp":1781876044336,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","funder":[{"name":"European Union","award":["101120657"],"award-info":[{"award-number":["101120657"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,29]]},"DOI":"10.1145\/3774905.3795079","type":"proceedings-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T17:14:56Z","timestamp":1779988496000},"page":"1001-1008","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["The LOPE Method: Improving Consistent Property Extraction for Scientific Knowledge Graphs Using LLMs"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3235-3042","authenticated-orcid":false,"given":"Sandra","family":"Schaftner","sequence":"first","affiliation":[{"name":"Chemnitz University of Technology, Chemnitz, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6729-2912","authenticated-orcid":false,"given":"Martin","family":"Gaedke","sequence":"additional","affiliation":[{"name":"Chemnitz University of Technology, Chemnitz, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,5,28]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","unstructured":"S\u00f6ren Auer Vinodh Ilangovan Markus Stocker Sanju Tiwari and Lars Vogt (Eds.). 2024. Open Research Knowledge Graph. Cuvillier Verlag G\u00f6ttingen. doi:10.34657\/13789","DOI":"10.34657\/13789"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1515\/bfp-2020-2042"},{"key":"e_1_3_2_1_3_1","volume-title":"Brown et al","author":"Tom","year":"2020","unstructured":"Tom B. Brown et al., 2020. Language Models are Few-Shot Learners. CoRR, Vol. abs\/2005.14165 (2020). arXiv:2005.14165"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2025.3554028"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3701716.3717820"},{"key":"e_1_3_2_1_6_1","volume-title":"Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference. arXiv:2403.04132","author":"Chiang Wei-Lin","year":"2024","unstructured":"Wei-Lin Chiang et al., 2024. Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference. arXiv:2403.04132 (2024). Leaderboard: https:\/\/lmarena.ai."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-025-05200-8"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109945"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","unstructured":"Jennifer D'Souza et al. 2024. Quality Assessment of Research Comparisons in the Open Research Knowledge Graph: A Case Study. JLIS.it Vol. 15 1 (Jan. 2024) 126-143. doi:10.36253\/jlis.it-547","DOI":"10.36253\/jlis.it-547"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3701716.3717817"},{"key":"e_1_3_2_1_11_1","volume-title":"Jiang et al","author":"Albert","year":"2024","unstructured":"Albert Q. Jiang et al., 2024a. Mixtral of Experts. arXiv:2401.04088 [cs.LG]"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.naacl-long.155"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/info13040161"},{"key":"e_1_3_2_1_14_1","unstructured":"Vamsi Krishna Kommineni Birgitta K\u00f6nig-Ries and Sheeba Samuel. 2024. From human experts to machines: An LLM supported approach to ontology and knowledge graph construction. arXiv:2403.08345 [cs.CL] https:\/\/arxiv.org\/abs\/2403.08345"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.15203\/99106-150-2-16"},{"key":"e_1_3_2_1_16_1","unstructured":"Guozheng Li Peng Wang and Wenjun Ke. 2023b. Revisiting Large Language Models as Zero-shot Relation Extractors. arXiv:2310.05028 [cs.AI] https:\/\/arxiv.org\/abs\/2310.05028"},{"key":"e_1_3_2_1_17_1","volume-title":"MNER: Enhanced Multimodal Named Entity Recognition with Auxiliary Refined Knowledge. arXiv:2305.12212 [cs.CL] https:\/\/arxiv.org\/abs\/2305.12212","author":"Jinyuan Li","year":"2023","unstructured":"Jinyuan Li et al., 2023a. Prompting ChatGPT in MNER: Enhanced Multimodal Named Entity Recognition with Auxiliary Refined Knowledge. arXiv:2305.12212 [cs.CL] https:\/\/arxiv.org\/abs\/2305.12212"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.deelio-1.10"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2018.07.017"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3253388"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.eacl-main.148"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","unstructured":"Vladyslav Nechakhin and Jennifer D'Souza. 2024. ORKG Properties and LLM-Generated Research Dimensions Evaluation Dataset. doi:10.25835\/6OYN9D1N","DOI":"10.25835\/6OYN9D1N"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.3390\/info15060328"},{"key":"e_1_3_2_1_24_1","volume-title":"How to implement LLM guardrails | OpenAI Cookbook. https:\/\/cookbook.openai.com\/examples\/how_to_use_guardrails Retrieved","author":"AI.","year":"2025","unstructured":"OpenAI. 2024a. How to implement LLM guardrails | OpenAI Cookbook. https:\/\/cookbook.openai.com\/examples\/how_to_use_guardrails Retrieved November 30, 2025 from"},{"key":"e_1_3_2_1_25_1","volume-title":"Prompt engineering | OpenAI API. https:\/\/platform.openai.com\/docs\/guides\/prompt-engineering Retrieved","author":"AI.","year":"2025","unstructured":"OpenAI. 2024b. Prompt engineering | OpenAI API. https:\/\/platform.openai.com\/docs\/guides\/prompt-engineering Retrieved November 30, 2025 from"},{"key":"e_1_3_2_1_26_1","unstructured":"OpenAI. 2025. Structured model outputs. https:\/\/platform.openai.com\/docs\/guides\/structured-outputs. Accessed: 2025-12-22."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3352100"},{"key":"e_1_3_2_1_28_1","unstructured":"Elvis Saravia. 2022. Prompt Engineering Guide. https:\/\/github.com\/dair-ai\/Prompt-Engineering-Guide DAIR.AI."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.3233\/FC-221513"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-022-00806-6"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.868"},{"key":"e_1_3_2_1_32_1","unstructured":"Zhen Wan et al. 2023. GPT-RE: In-context Learning for Relation Extraction using Large Language Models. arXiv:2305.02105 [cs.CL] https:\/\/arxiv.org\/abs\/2305.02105"},{"key":"e_1_3_2_1_33_1","unstructured":"Xinyu Wang et al. 2022. Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning. arXiv:2105.03654 [cs.CL] https:\/\/arxiv.org\/abs\/2105.03654"},{"key":"e_1_3_2_1_34_1","unstructured":"Xiang Wei et al. 2024. ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT. arXiv:2302.10205 [cs.CL] https:\/\/arxiv.org\/abs\/2302.10205"},{"key":"e_1_3_2_1_35_1","volume-title":"Prompt Engineering. https:\/\/lilianweng.github.io\/posts\/2023-03-15-prompt-engineering\/ Retrieved","author":"Weng Lilian","year":"2025","unstructured":"Lilian Weng. 2023. Prompt Engineering. https:\/\/lilianweng.github.io\/posts\/2023-03-15-prompt-engineering\/ Retrieved November 30, 2025 from"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3701716.3717821"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543873.3587318"},{"key":"e_1_3_2_1_38_1","unstructured":"Xin Zhang et al. 2022. Domain-Specific NER via Retrieving Correlated Samples. arXiv:2208.12995 [cs.CL]"},{"key":"e_1_3_2_1_39_1","volume-title":"Zhao et al","author":"Tony","year":"2021","unstructured":"Tony Z. Zhao et al., 2021. Calibrate Before Use: Improving Few-Shot Performance of Language Models. arXiv:2102.09690 [cs.CL]"},{"key":"e_1_3_2_1_40_1","unstructured":"Wayne Xin Zhao et al. 2025. A Survey of Large Language Models. arXiv:2303.18223 [cs.CL]"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Yuqi Zhu et al. 2024. LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities. arXiv:2305.13168 [cs.CL]","DOI":"10.1007\/s11280-024-01297-w"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-94578-6_910.1007\/978-3-031-94578-6_9"}],"event":{"name":"WWW '26: The ACM Web Conference 2026","location":"Dubai United Arab Emirates","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Companion Proceedings of the ACM Web Conference 2026"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3774905.3795079","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T17:20:15Z","timestamp":1779988815000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774905.3795079"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,28]]},"references-count":42,"alternative-id":["10.1145\/3774905.3795079","10.1145\/3774905"],"URL":"https:\/\/doi.org\/10.1145\/3774905.3795079","relation":{},"subject":[],"published":{"date-parts":[[2026,5,28]]},"assertion":[{"value":"2026-05-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}