{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T09:57:12Z","timestamp":1781690232939,"version":"3.54.5"},"reference-count":51,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T00:00:00Z","timestamp":1780531200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T00:00:00Z","timestamp":1780531200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC3301800"],"award-info":[{"award-number":["2022YFC3301800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Relation extraction, as a crucial component of knowledge engineering, is essential to the automated construction of knowledge\u2010based expert systems. The existing methods ignore the important phrase information, and other useful information in the sentence is not fully utilised. To address these problems, we propose the phrase information enhanced and relation guided relation extraction, named PIERG. This method uses a multi\u2010scale convolutional neural network, which is sensitive to local context, to obtain multi\u2010granularity phrase features, and applies the gating mechanism to filter out the noise information in a sentence and find out the key phrase features that trigger relation types. In addition, an attentional network enhanced by entity pairs and piecewise information is designed to learn more useful information related to relation types. Finally, by calculating the correlation score between the decomposed relation labels and the head entity, the tail entity and the final sentence vector representation, the relation types can be more effectively located. The experiments demonstrate that PIERG can achieve better extraction results than most previous methods. Therefore, PIERG can serve as an effective technical pathway for automated knowledge acquisition and knowledge base filling in expert systems, substantially reducing manual effort in structured knowledge engineering.<\/jats:p>","DOI":"10.1111\/exsy.70323","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:40:34Z","timestamp":1780620034000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Phrase Information Enhanced and Relation Guided Relation Extraction"],"prefix":"10.1111","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3884-8440","authenticated-orcid":false,"given":"Tiantian","family":"Chen","sequence":"first","affiliation":[{"name":"School of Cyber Security and Computer Hebei University  Baoding China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meiling","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Cyber Security and Computer Hebei University  Baoding China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1242-8375","authenticated-orcid":false,"given":"Zihao","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence Hebei Oriental University  Langfang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nianbin","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology Harbin Engineering University  Harbin China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,4]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/8889075"},{"key":"e_1_2_9_3_1","first-page":"4171","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"Devlin J.","year":"2019"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110195"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.09.006"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105912"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2022.04.019"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1247"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12042"},{"key":"e_1_2_9_10_1","first-page":"541","volume-title":"Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies","author":"Hoffmann R.","year":"2011"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-42914-9_1"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1191"},{"key":"e_1_2_9_13_1","unstructured":"Jat S. 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