{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T06:01:25Z","timestamp":1770444085765,"version":"3.49.0"},"reference-count":41,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["71971031"],"award-info":[{"award-number":["71971031"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Various plant attributes, such as growing environment, growth cycle, and ecological distribution, can provide support to fields like agricultural production and biodiversity. This information is widely dispersed in texts. Manual extraction of this information is highly inefficient due to a fact that it not only takes considerable time but also increases the likelihood of overlooking relevant details. To convert textual data into structured information, we extract relational triples in the form of (subject, relation, object), where the subject represents the names of plants, the object represents the plant attributes, and the relation represents the classification of plant attributes. To reduce complexity, we employ a joint extraction of entities and relations based on a tagging scheme. The task is broken down into three parts. Firstly, a matrix is used to simultaneously match plant entities and plant attributes. Then, the predefined categories of plant attributes are classified. Finally, the categories of plant attributes are matched with entity-attribute pairs. The tagging-based method typically utilizes parameter sharing to facilitate interaction between different tasks, but it can also lead to issues such as error amplification and instability in parameter updates. Thus, we adopt improved techniques at different stages to enhance the performance of our model. This includes adjustment to the word embedding layer of BERT and optimization in relation prediction. The modification of the word embedding layer is intended to better integrate contextual information during text representation and reduce the interference of erroneous information. The relation prediction part mainly involves multi-level information fusion of textual information, thereby making corrections and highlighting important information. We name the three-stage method as \u201cBwdgv\u201d. Compared to the currently advanced PRGC model, the F1-score of the proposed method has an improvement of 1.4%. With the help of extracted triples, we can construct knowledge graphs and other tasks to better apply various plant attributes.<\/jats:p>","DOI":"10.1371\/journal.pone.0327186","type":"journal-article","created":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T13:23:41Z","timestamp":1751981021000},"page":"e0327186","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Plant attribute extraction: An enhancing three-stage deep learning model for relational triple extraction"],"prefix":"10.1371","volume":"20","author":[{"given":"Zhihao","family":"Zong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongtao","family":"Shan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5513-4100","authenticated-orcid":true,"given":"Gaoyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6186-9162","authenticated-orcid":true,"given":"George Xianzhi","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuyi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2025,7,8]]},"reference":[{"issue":"1","key":"pone.0327186.ref001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00425-020-03501-3","article-title":"Light- and hormone-mediated development in non-flowering plants: An overview","volume":"253","author":"DP Biswal","year":"2020","journal-title":"Planta"},{"issue":"7","key":"pone.0327186.ref002","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/j.baae.2011.09.006","article-title":"Size-based food web characteristics govern the response to species extinctions","volume":"12","author":"JO Riede","year":"2011","journal-title":"Basic and Applied Ecology"},{"issue":"1","key":"pone.0327186.ref003","doi-asserted-by":"crossref","first-page":"40","DOI":"10.5958\/0974-0112.2017.00011.1","article-title":"Influence of plant bioregulators on growth, yield and physico-chemical characteristics of strawberry","volume":"74","author":"TK Hazarika","year":"2017","journal-title":"Ind Jour of Hort"},{"issue":"2","key":"pone.0327186.ref004","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1142\/S0192415X20500147","article-title":"A Review of Medicinal Plants in Cardiovascular Disorders: Benefits and Risks","volume":"48","author":"M Naveed","year":"2020","journal-title":"Am J Chin Med"},{"issue":"1","key":"pone.0327186.ref005","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41438-021-00499-x","article-title":"Research advances in and prospects of ornamental plant genomics","volume":"8","author":"T Zheng","year":"2021","journal-title":"Hortic Res"},{"issue":"2","key":"pone.0327186.ref006","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/0378-8741(80)90004-5","article-title":"The present status of knowledge of the plants used in traditional medicine in Western Africa: a medical approach and a chemical evaluation","volume":"2","author":"A Sofowora","year":"1980","journal-title":"J Ethnopharmacol"},{"key":"pone.0327186.ref007","doi-asserted-by":"crossref","first-page":"106049","DOI":"10.1016\/j.ecoleng.2020.106049","article-title":"Using ecological knowledge for landscaping with plants in cities","volume":"158","author":"S Tabassum","year":"2020","journal-title":"Ecological Engineering"},{"key":"pone.0327186.ref008","unstructured":"Rusu D, Dali L, Fortuna B, Grobelnik M, Mladenic D. Triplet extraction from sentences. In: Proceedings of the 10th International Multiconference: Information Society-IS, 2007. 8\u201312."},{"key":"pone.0327186.ref009","volume-title":"Joint extraction of events and entities within a document context. arXiv","author":"B Yang","year":"2016"},{"key":"pone.0327186.ref010","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.1016\/j.procs.2023.01.155","article-title":"A Natural Language Processing System using CWS Pipeline for Extraction of Linguistic Features","volume":"218","author":"S Kumar","year":"2023","journal-title":"Procedia Computer Science"},{"issue":"1","key":"pone.0327186.ref011","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1017\/S1351324916000334","article-title":"Word2Vec","volume":"23","author":"KW Church","year":"2017","journal-title":"Nat Lang Eng"},{"key":"pone.0327186.ref012","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning C. GloVe: Global Vectors for Word Representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014. 1532\u201343.","DOI":"10.3115\/v1\/D14-1162"},{"key":"pone.0327186.ref013","volume-title":"Deep contextualized word representations for detecting sarcasm and irony. arXiv","author":"S Ili\u0107","year":"2018"},{"key":"pone.0327186.ref014","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","author":"J Devlin","year":"2018"},{"key":"pone.0327186.ref015","volume-title":"Attention Is All You Need. arXiv","author":"A Vaswani","year":"2017"},{"key":"pone.0327186.ref016","doi-asserted-by":"crossref","unstructured":"Liu C, Zhu C, Zhu W. Chinese Named Entity Recognition Based on BERT with Whole Word Masking. In: Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence, 2020. 311\u20136. doi: 10.1145\/3404555.3404563","DOI":"10.1145\/3404555.3404563"},{"key":"pone.0327186.ref017","volume-title":"Domain-Adversarial Training of Neural Networks","author":"Y Ganin","year":"2015"},{"key":"pone.0327186.ref018","volume-title":"Joint extraction of entities and relations based on a novel decomposition strategy. arXiv","author":"B Yu","year":"2019"},{"key":"pone.0327186.ref019","volume-title":"A novel cascade binary tagging framework for relational triple extraction. arXiv","author":"Z Wei","year":"2019"},{"key":"pone.0327186.ref020","volume-title":"TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking. arXiv","author":"Y Wang","year":"2020"},{"key":"pone.0327186.ref021","volume-title":"OneRel: Joint Entity and Relation Extraction with One Module in One Step. arXiv","author":"Y Shang","year":"2022"},{"key":"pone.0327186.ref022","doi-asserted-by":"crossref","unstructured":"Zelenko D, Aone C, Richardella A. Kernel methods for relation extraction. In: Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP \u201902, 2002. 71\u20138. doi: 10.3115\/1118693.1118703","DOI":"10.3115\/1118693.1118703"},{"key":"pone.0327186.ref023","volume-title":"CoType: Joint extraction of typed entities and relations with knowledge bases","author":"X Ren","year":"2016"},{"key":"pone.0327186.ref024","volume-title":"End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures. arXiv","author":"M Miwa","year":"2016"},{"key":"pone.0327186.ref025","volume-title":"Joint extraction of entities and relations based on a novel tagging scheme. arXiv","author":"S Zheng","year":"2017"},{"key":"pone.0327186.ref026","doi-asserted-by":"crossref","unstructured":"Katiyar A, Cardie C. Going out on a limb: Joint extraction of entity mentions and relations without dependency trees. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2017. 917\u201328.","DOI":"10.18653\/v1\/P17-1085"},{"key":"pone.0327186.ref027","volume-title":"PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction. arXiv","author":"H Zheng","year":"2021"},{"key":"pone.0327186.ref028","doi-asserted-by":"crossref","first-page":"128744","DOI":"10.1016\/j.ufug.2025.128744","article-title":"The post-effects of landscape practices on spontaneous plants in urban parks","volume":"107","author":"C Chen","year":"2025","journal-title":"Urban Forestry & Urban Greening"},{"issue":"8","key":"pone.0327186.ref029","article-title":"A corpus of plant-disease relations in the biomedical domain","volume":"14","author":"B Kim","year":"2019","journal-title":"PLoS One"},{"issue":"1","key":"pone.0327186.ref030","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1186\/s12859-017-1857-8","article-title":"A method for named entity normalization in biomedical articles: application to diseases and plants","volume":"18","author":"H Cho","year":"2017","journal-title":"BMC Bioinformatics"},{"issue":"3","key":"pone.0327186.ref031","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1109\/TAI.2024.3491938","article-title":"Multiattribute Deep CNN-Based Approach for Detecting Medicinal Plants and Their Use for Skin Diseases","volume":"6","author":"P Dalvi","year":"2025","journal-title":"IEEE Trans Artif Intell"},{"issue":"9","key":"pone.0327186.ref032","doi-asserted-by":"crossref","first-page":"2130","DOI":"10.3390\/agronomy12092130","article-title":"Research on the Chinese Named-Entity\u2013Relation-Extraction Method for Crop Diseases Based on BERT","volume":"12","author":"W Zhang","year":"2022","journal-title":"Agronomy"},{"issue":"7","key":"pone.0327186.ref033","first-page":"1208","article-title":"Extracting entity and relation of landscape plant knowledge based on ALBERT model","volume":"23","author":"XL Chen","year":"2021","journal-title":"Journal of Geo-information Science"},{"issue":"2","key":"pone.0327186.ref034","doi-asserted-by":"crossref","first-page":"354","DOI":"10.3390\/agronomy15020354","article-title":"Research and Implementation of Agronomic Entity and Attribute Extraction Based on Target Localization","volume":"15","author":"X Guo","year":"2025","journal-title":"Agronomy"},{"key":"pone.0327186.ref035","volume-title":"Adversarial training for multi-context joint entity and relation extraction. arXiv","author":"G Bekoulis","year":"2018"},{"key":"pone.0327186.ref036","volume-title":"Adversarial Training Methods for Semi-Supervised Text Classification. arXiv","author":"T Miyato","year":"2016"},{"issue":"6","key":"pone.0327186.ref037","article-title":"Concerning the vegetation Chinese regionalization map on as a part of in the natural geographical atlas of the People\u2019s Republic of China","author":"S ShiZhou","year":"1998","journal-title":"Journal of Plant Ecology"},{"key":"pone.0327186.ref038","volume-title":"Garden Plant Science","author":"H Yujie","year":"2013"},{"key":"pone.0327186.ref039","doi-asserted-by":"crossref","unstructured":"Yuan Y, Zhou X, Pan S, Zhu Q, Song Z, Guo L. A relation-specific attention network for joint entity and relation extraction. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Yokohama, Yokohama, Japan, 2021. 561.","DOI":"10.24963\/ijcai.2020\/561"},{"key":"pone.0327186.ref040","volume-title":"UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction","author":"W Tang","year":"2022"},{"key":"pone.0327186.ref041","volume-title":"A novel global feature-oriented relational triple extraction model based on table filling. arXiv","author":"F Ren","year":"2021"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0327186","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T13:23:57Z","timestamp":1751981037000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0327186"}},"subtitle":[],"editor":[{"given":"Jin","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2025,7,8]]},"references-count":41,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7,8]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0327186","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,8]]}}}