{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:12:51Z","timestamp":1760058771681,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T00:00:00Z","timestamp":1745884800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC3209201","62401196","BK20241508"],"award-info":[{"award-number":["2023YFC3209201","62401196","BK20241508"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023YFC3209201","62401196","BK20241508"],"award-info":[{"award-number":["2023YFC3209201","62401196","BK20241508"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["2023YFC3209201","62401196","BK20241508"],"award-info":[{"award-number":["2023YFC3209201","62401196","BK20241508"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Distantly Supervised Relation Extraction (DSRE) aims to automatically identify semantic relationships within large text corpora by aligning with external knowledge bases. Despite the success of current methods in automating data annotation, they introduce two main challenges: label noise and data long-tail distribution. Label noise results in inaccurate annotations, which can undermine the quality of relation extraction. The long-tail problem, on the other hand, leads to an imbalanced model that struggles to extract less frequent, long-tail relations. In this paper, we introduce a novel relation extraction framework based on multi-level hierarchical attention. This approach utilizes Graph Attention Networks (GATs) to model the hierarchical structure of the relations, capturing the semantic dependencies between relation types and generating relation embeddings that reflect the overall hierarchical framework. To improve the classification process, we incorporate a multi-level classification structure guided by hierarchical attention, which enhances the accuracy of both head and tail relation extraction. A local probability constraint is introduced to ensure coherence across the classification levels, fostering knowledge transfer from frequent to less frequent relations. Experimental evaluations on the New York Times (NYT) dataset demonstrate that our method outperforms existing baselines, particularly in the context of long-tail relation extraction, offering a comprehensive solution to the challenges of DSRE.<\/jats:p>","DOI":"10.3390\/info16050364","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T11:00:54Z","timestamp":1745924454000},"page":"364","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Distantly Supervised Relation Extraction Method Based on Multi-Level Hierarchical Attention"],"prefix":"10.3390","volume":"16","author":[{"given":"Zhaoxin","family":"Xuan","sequence":"first","affiliation":[{"name":"Department of Technological Innovation, COFCO Corporation, Beijing 100020, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hejing","family":"Zhao","sequence":"additional","affiliation":[{"name":"Research Center on Flood and Drought Disaster Reduction of Ministry of Water Resource, China Institute of Water Resources and Hydropower Research, Beijing 100038, China"},{"name":"Water History Department, China Institute of Water Resources and Hydropower Research, Beijing 100038, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0576-3181","authenticated-orcid":false,"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Hohai University, Nanjing 211100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziqi","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Earth System Science, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1709","DOI":"10.3390\/ai5030084","article-title":"Improving Distantly Supervised Relation Extraction with Multi-Level Noise Reduction","volume":"5","author":"Song","year":"2024","journal-title":"AI"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Knez, T., \u0160travs, M., and \u017ditnik, S. 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