{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T19:54:11Z","timestamp":1791402851323,"version":"4.3.3"},"reference-count":54,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2026.3722487","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T19:15:04Z","timestamp":1786389304000},"page":"151991-152009","source":"Crossref","is-referenced-by-count":0,"title":["CGNN-MGS: A Cascaded Heterogeneous Graph Neural Network With Meta-Path Ring Structures for Joint Modeling of Contract Clause Text and Structural Dependencies"],"prefix":"10.1109","volume":"14","author":[{"given":"Xi","family":"Liu","sequence":"first","affiliation":[{"name":"Engineering University of the Joint Logistics Support Force, PLA","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1725-3770","authenticated-orcid":false,"given":"Ru","family":"Wang","sequence":"additional","affiliation":[{"name":"Engineering University of the Joint Logistics Support Force, PLA","place":["Chongqing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","volume-title":"13th Annual Construction Disputes Report 2023","year":"2023"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CO.1943-7862.0002122"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2025.104251"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2022.104465"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2023.104890"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2023.103940"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2024.104082"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2025.106179"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s44443-026-00605-w"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2025.118794"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)LA.1943-4170.0000498"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380297"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380027"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CP.1943-5487.0000346"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CP.1943-5487.0000807"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CO.1943-7862.0000131"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CP.1943-5487.0000301"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)co.1943-7862.0002275"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1061\/JCCEE5.CPENG-5218"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1108\/ECAM-02-2023-0172"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)ME.1943-5479.0000757"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CDMA47397.2020.00027"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3390\/su14116938"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2023.105200"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.24818\/ida-ql\/2019.5"},{"key":"ref28","first-page":"1877","article-title":"Language models are few-shot learners","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Brown"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1800"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.52202\/075280-0517"},{"key":"ref31","article-title":"A survey of large language models","author":"Zhao","year":"2023","journal-title":"arXiv:2303.18223"},{"key":"ref32","article-title":"Augmented language models: A survey","author":"Mialon","year":"2023","journal-title":"arXiv:2302.07842"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2979670"},{"key":"ref34","article-title":"Lawyer LLaMA technical report","volume-title":"arXiv:2305.15062","author":"Huang","year":"2023"},{"key":"ref35","article-title":"ChatLaw: Open-source legal large language model with integrated external knowledge bases","author":"Cui","year":"2023","journal-title":"arXiv:2306.16092"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3360454"},{"key":"ref37","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Kipf"},{"key":"ref38","first-page":"1","article-title":"Graph attention networks","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Veli\u010dkovi\u0107"},{"key":"ref39","first-page":"1","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467350"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26283"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583493"},{"key":"ref44","first-page":"1","article-title":"Graph transformer networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Yun"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3239842"},{"key":"ref46","article-title":"How powerful are graph neural networks?","author":"Xu","year":"2018","journal-title":"arXiv:1810.00826"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014602"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3154319"},{"key":"ref49","first-page":"2625","article-title":"Weisfeiler and lehman go cellular: CW networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Bodnar"},{"key":"ref50","first-page":"1","article-title":"On the equivalence between graph isomorphism testing and function approximation with GNNs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chen"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2009.12.006"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)LA.1943-4170.0000505"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645515"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3264691"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11647025.pdf?arnumber=11647025","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T19:11:40Z","timestamp":1791400300000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11647025\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":54,"URL":"https:\/\/doi.org\/10.1109\/access.2026.3722487","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}