{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:37:52Z","timestamp":1761176272292,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Few-shot relation extraction aims to identify and classify specific semantic relations between entities from text with a small number of annotated examples. Recent studies have shown that innovative model designs and learning strategies can significantly enhance the model\u2019s generalization ability and performance, even with limited training samples. However, the samples used for training face the dual challenges of scarce annotated data and semantic ambiguity. Given the limitations of the existing SaCon framework in negative sample generation and discrimination efficiency, we propose a dynamic adversarial negative sample enhancement strategy. This strategy introduces adversarial perturbations before encoding the pre-trained language model by constructing a multi-granularity semantic space. Specifically, we first design an entity permutation mechanism to randomly exchange the subject\/object entities of sentences in a small batch to generate negative sample clusters with similar semantics but misplaced relations, then, we integrate a multi-view contrastive learning framework to embed adversarial samples into the feature space topology optimization process.To strengthen boundary-sensitive features, an adaptive margin ranking loss function is proposed to dynamically adjust the representation distance constraints of positive and negative samples, forcing the model to capture the deep semantic invariance of relational predicates under limited samples. This method aims to optimize the traditional negative sample random sampling paradigm, actively explore the semantic space through an adversarial generation mechanism, and construct \u201cdifficult samples\u201d with minimal semantic deviation through gradient back propagation, thereby improving the model\u2019s ability to parse implicit relational patterns. The experiment result shows that this framework effectively alleviates the risk of overfitting in small sample scenarios by decoupling relational semantics and surface syntactic features, and its dynamic loss design provides mathematical guarantees for orthogonal separation of feature space. Our code is available online at https:\/\/github.com\/hhy-test\/ESCR.<\/jats:p>","DOI":"10.3233\/faia251324","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:58:11Z","timestamp":1761127091000},"source":"Crossref","is-referenced-by-count":0,"title":["Few-Shot Relation Extraction via Semantically Related Negative Samples"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7195-3413","authenticated-orcid":false,"given":"Delong","family":"Han","sequence":"first","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"},{"name":"Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2058-5490","authenticated-orcid":false,"given":"Hongyu","family":"Hao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"},{"name":"Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9245-0110","authenticated-orcid":false,"given":"Jin","family":"Wan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"},{"name":"Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7896-4833","authenticated-orcid":false,"given":"Gang","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"},{"name":"Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0507-5576","authenticated-orcid":false,"given":"Min","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"},{"name":"Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4911-276X","authenticated-orcid":false,"given":"Mingle","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"},{"name":"Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251324","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:58:11Z","timestamp":1761127091000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251324"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251324","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}