{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T06:01:13Z","timestamp":1776146473775,"version":"3.50.1"},"reference-count":36,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202202"],"award-info":[{"award-number":["62202202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Data & Knowledge Engineering"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.datak.2026.102585","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T07:52:20Z","timestamp":1773906740000},"page":"102585","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A knowledge fusion method based on product review relation extraction"],"prefix":"10.1016","volume":"164","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5408-5594","authenticated-orcid":false,"given":"Cao","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3605-7984","authenticated-orcid":false,"given":"Ning","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5177-0812","authenticated-orcid":false,"given":"Jun","family":"Xiang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2378-2266","authenticated-orcid":false,"given":"Ruru","family":"Pan","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.datak.2026.102585_bib0001","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1109\/TNNLS.2021.3070843","article-title":"A survey on knowledge graphs: representation, acquisition, and applications","volume":"33","author":"Ji","year":"2022","journal-title":"IEEe Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.datak.2026.102585_bib0002","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.jksuci.2018.08.005","article-title":"A literature review on question answering techniques, paradigms and systems","volume":"32","author":"Soares","year":"2020","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"10.1016\/j.datak.2026.102585_bib0003","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1007\/s10462-021-10031-1","article-title":"Arabic question answering system: a survey","volume":"55","author":"Alwaneen","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.datak.2026.102585_bib0004","doi-asserted-by":"crossref","DOI":"10.1145\/3604931","article-title":"Named entity recognition and classification in historical documents: a survey","volume":"56","author":"Ehrmann","year":"2024","journal-title":"ACM. Comput. Surv."},{"key":"10.1016\/j.datak.2026.102585_bib0005","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1007\/s12559-021-09917-7","article-title":"Deep neural approaches to relation triplets extraction: a comprehensive survey","volume":"13","author":"Nayak","year":"2021","journal-title":"Cognit. Comput."},{"key":"10.1016\/j.datak.2026.102585_bib0006","unstructured":"X. Luo, W. Liu, M. Ma, P. Wang, A bidirectional tree tagging scheme for joint medical relation extraction, in, 2020, pp. arXiv:2008.13339. https:\/\/doi.org\/10.48550\/arXiv.2008.13339."},{"key":"10.1016\/j.datak.2026.102585_bib0007","doi-asserted-by":"crossref","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","article-title":"Knowledge graph embedding: a survey of approaches and applications","volume":"29","author":"Wang","year":"2017","journal-title":"IEEe Trans. Knowl. Data Eng."},{"key":"10.1016\/j.datak.2026.102585_bib0008","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.eswa.2018.07.032","article-title":"Joint entity recognition and relation extraction as a multi-head selection problem","volume":"114","author":"Bekoulis","year":"2018","journal-title":"Expert. Syst. Appl."},{"key":"10.1016\/j.datak.2026.102585_bib0009","doi-asserted-by":"crossref","unstructured":"Z. Wei, J. Su, Y. Wang, Y. Tian, Y. Chang, A novel cascade binary tagging framework for relational triple extraction, Association for Computational Linguistics, Online, 2020. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.136.","DOI":"10.18653\/v1\/2020.acl-main.136"},{"key":"10.1016\/j.datak.2026.102585_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.108604","article-title":"Joint entity and relation extraction with position-aware attention and relation embedding","volume":"119","author":"Chen","year":"2022","journal-title":"Appl. Soft. Comput."},{"key":"10.1016\/j.datak.2026.102585_bib0011","first-page":"271","article-title":"ERGM: a multi-stage joint entity and relation extraction with global entity match","author":"Gao","year":"2023","journal-title":"Knowl. Based. Syst."},{"key":"10.1016\/j.datak.2026.102585_bib0012","series-title":"Proceedings of the ACL-02 conference on Empirical methods in natural language processing","first-page":"71","article-title":"Kernel methods for relation extraction","volume":"10","author":"Zelenko","year":"2002"},{"key":"10.1016\/j.datak.2026.102585_bib0013","series-title":"Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics","first-page":"427","article-title":"Exploring various knowledge in relation extraction","author":"GuoDong","year":"2005"},{"key":"10.1016\/j.datak.2026.102585_bib0014","unstructured":"B. Yu, Z. Zhang, X. Shu, Y. Wang, T. Liu, B. Wang, S. Li, Joint extraction of entities and relations based on a novel decomposition strategy, in, 2019, pp. arXiv:1909.04273. https:\/\/doi.org\/10.48550\/arXiv.1909.04273."},{"key":"10.1016\/j.datak.2026.102585_bib0015","series-title":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","first-page":"824","article-title":"A simple but effective bidirectional framework for relational triple extraction","author":"Ren","year":"2022"},{"key":"10.1016\/j.datak.2026.102585_bib0016","first-page":"236","article-title":"Three birds, one stone: a novel translation based framework for joint entity and relation extraction","author":"Huang","year":"2022","journal-title":"Knowl. Based. Syst."},{"key":"10.1016\/j.datak.2026.102585_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.csl.2024.101749","article-title":"TR-Net: token relation inspired table filling network for joint entity and relation extraction","volume":"90","author":"Kong","year":"2025","journal-title":"Comput. Speech Language"},{"key":"10.1016\/j.datak.2026.102585_bib0018","series-title":"Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism","author":"Zeng","year":"2018"},{"key":"10.1016\/j.datak.2026.102585_bib0019","series-title":"Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning","author":"Zeng","year":"2019"},{"key":"10.1016\/j.datak.2026.102585_bib0020","first-page":"9507","article-title":"CopyMTL: copy mechanism for joint extraction of entities and relations with Multi-Task learning","volume":"34","author":"Zeng","year":"2020","journal-title":"Proceed. AAAI Conf. Artificial Intell."},{"key":"10.1016\/j.datak.2026.102585_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109129","article-title":"Boundary assembling method for joint entity and relation extraction","volume":"250","author":"Tang","year":"2022","journal-title":"Knowl. Based. Syst."},{"key":"10.1016\/j.datak.2026.102585_bib0022","article-title":"TPLinker: single-stage joint extraction of entities and relations through token pair linking","author":"Wang","year":"2020","journal-title":"Int. Committee Comput. Linguist."},{"key":"10.1016\/j.datak.2026.102585_bib0023","series-title":"A Novel Global Feature-Oriented Relational Triple Extraction Model based on Table Filling","author":"Ren","year":"2021"},{"key":"10.1016\/j.datak.2026.102585_bib0024","first-page":"11285","article-title":"OneRel: joint entity and relation extraction with one module in one step","volume":"36","author":"Shang","year":"2022","journal-title":"Proceed. AAAI Conf. Artif. Intell."},{"key":"10.1016\/j.datak.2026.102585_bib0025","doi-asserted-by":"crossref","first-page":"29656","DOI":"10.1007\/s10489-023-05111-4","article-title":"Joint relational triple extraction based on potential relation detection and conditional entity mapping","volume":"53","author":"Zhou","year":"2023","journal-title":"Appl. Intell."},{"key":"10.1016\/j.datak.2026.102585_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106479","article-title":"Triplet-aware graph neural networks for factorized multi-modal knowledge graph entity alignment","volume":"179","author":"Li","year":"2024","journal-title":"Neural Networks"},{"key":"10.1016\/j.datak.2026.102585_bib0027","doi-asserted-by":"crossref","unstructured":"Q. Li, C. Ji, S. Guo, K. Peng, Q. Mao, S. Wang, Variational multi-modal hypergraph attention network for multi-modal relation extraction, in: K. James (Ed.) Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (IJCAI-25), International Joint Conferences on Artificial Intelligence Organization, Macao, China, 2025, pp. 8159\u20138167. https:\/\/doi.org\/10.24963\/ijcai.2025\/907.","DOI":"10.24963\/ijcai.2025\/907"},{"key":"10.1016\/j.datak.2026.102585_bib0028","doi-asserted-by":"crossref","first-page":"6633","DOI":"10.1007\/s40747-024-01518-9","article-title":"Joint entity and relation extraction combined with multi-module feature information enhancement","volume":"10","author":"Li","year":"2024","journal-title":"Complex Intell. Syst."},{"key":"10.1016\/j.datak.2026.102585_bib0029","series-title":"Modeling Relations and Their Mentions without Labeled Text","first-page":"148","author":"Riedel","year":"2010"},{"key":"10.1016\/j.datak.2026.102585_bib0030","series-title":"Creating Training Corpora For NLG Micro-Planners","first-page":"179","author":"Gardent","year":"2017"},{"key":"10.1016\/j.datak.2026.102585_bib0031","article-title":"Joint extraction of entities and relations based on a novel tagging scheme","author":"Zheng","year":"2017","journal-title":"ArXiv."},{"key":"10.1016\/j.datak.2026.102585_bib0032","series-title":"GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction","author":"Fu","year":"2019"},{"key":"10.1016\/j.datak.2026.102585_bib0033","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1007\/s10115-024-02241-0","article-title":"Bidirectional network-based relational triple extraction with prior relation mechanism","volume":"67","author":"Xiao","year":"2025","journal-title":"Knowl. Inf. Syst."},{"key":"10.1016\/j.datak.2026.102585_bib0034","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s41019-024-00268-5","article-title":"Relation semantic guidance and entity position location for Relation extraction","volume":"10","author":"Chen","year":"2025","journal-title":"Data Sci. Eng."},{"key":"10.1016\/j.datak.2026.102585_bib0035","series-title":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, Association for Computing Machinery","first-page":"701","article-title":"DeepWalk: online learning of social representations","author":"Perozzi","year":"2014"},{"key":"10.1016\/j.datak.2026.102585_bib0036","doi-asserted-by":"crossref","unstructured":"J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, Q. Mei, LINE: large-scale information network embedding, arXiv e-prints, (2015) arXiv:1503.03578. https:\/\/doi.org\/10.48550\/arXiv.1503.03578.","DOI":"10.1145\/2736277.2741093"}],"container-title":["Data & Knowledge Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169023X26000327?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169023X26000327?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T05:19:39Z","timestamp":1776143979000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169023X26000327"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":36,"alternative-id":["S0169023X26000327"],"URL":"https:\/\/doi.org\/10.1016\/j.datak.2026.102585","relation":{},"ISSN":["0169-023X"],"issn-type":[{"value":"0169-023X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A knowledge fusion method based on product review relation extraction","name":"articletitle","label":"Article Title"},{"value":"Data & Knowledge Engineering","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.datak.2026.102585","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"102585"}}