{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T04:50:44Z","timestamp":1773031844036,"version":"3.50.1"},"reference-count":23,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T00:00:00Z","timestamp":1747872000000},"content-version":"vor","delay-in-days":141,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>Bridge inspection text records are very significant for the maintenance and upkeep of bridges, which can help engineers and maintenance personnel to understand the actual condition of bridges, detect and repair problems in time, and ensure the safe operation of bridges. Currently, more and more research focuses on how to extract potentially valuable bridge\u2010related information from bridge inspection texts. In this study, we take the bridge inspection domain machine\u2010reading comprehension corpus as the data support for model training and performance evaluation; oriented to the bridge inspection domain data text extraction machine\u2010reading comprehension task, on the basis of word\u2010granularity text input, we further explore two schemes of co\u2010occurring linkage of cross\u2010sentence entities in the context and co\u2010occurring linkage of entities within the sentence through graph structure, and we learn and extract naming through graph\u2010attentive neural networks\u2010structured semantic information between entities and fused the obtained named entity embeddings with a hidden representation of the pretrained context. Tested on the bridge inspection domain dataset, the integrated model proposed in this research improves the EM optimum by 1.4% and the mean by 2.2% and the F1 optimum by 2.2% and the mean by 1.6% on the BIQA test, compared with the better\u2010performing baseline model RoBERTa_wwm_ext.<\/jats:p>","DOI":"10.1155\/cplx\/6691354","type":"journal-article","created":{"date-parts":[[2025,5,22]],"date-time":"2025-05-22T10:18:29Z","timestamp":1747909109000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine\u2010Reading Comprehension for Bridge Inspection Domain by Fusing Graph Embedding"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4962-4581","authenticated-orcid":false,"given":"Fangyue","family":"Xiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjin","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"YuFang","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maobo","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenyong","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,5,22]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2020.2978386"},{"key":"e_1_2_11_2_2","first-page":"1","article-title":"Applications of Graph Neural Network for Natural Language Processing","volume":"35","author":"Yulong C.","year":"2021","journal-title":"Journal of Chinese Information Processing"},{"key":"e_1_2_11_3_2","first-page":"150","article-title":"Survey on Large-Scale Graph Neural Network Systems","volume":"33","author":"Gang Z.","year":"2021","journal-title":"Software Journal"},{"key":"e_1_2_11_4_2","doi-asserted-by":"crossref","unstructured":"ChenZ. M. WeiX. S. WangP. andGuoY. Multi-label Image Recognition with Graph Convolutional Networks Proceedings of the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition April 2019 5177\u20135186.","DOI":"10.1109\/CVPR.2019.00532"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33016236"},{"key":"e_1_2_11_6_2","article-title":"Graph Attention Networks","author":"Veli\u02c7ckovic P.","year":"2017","journal-title":"arXiv preprint arXiv:1710.10903"},{"key":"e_1_2_11_7_2","doi-asserted-by":"crossref","unstructured":"RageshR. SellamanickamS. IyerA. BairiR. andLingamV. 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