{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T09:27:25Z","timestamp":1778405245469,"version":"3.51.4"},"reference-count":61,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:00:00Z","timestamp":1761091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Acupuncture, a key modality in traditional Chinese medicine, is gaining global recognition as a complementary therapy and a subject of increasing scientific interest. However, fragmented and unstructured acupuncture knowledge spread across diverse sources poses challenges for semantic retrieval, reasoning, and in-depth analysis. To address this gap, we developed AcuKG, a comprehensive knowledge graph that systematically organizes acupuncture-related knowledge to support sharing, discovery, and artificial intelligence\u2013driven innovation in the field.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>AcuKG integrates data from multiple sources, including online resources, guidelines, PubMed literature, ClinicalTrials.gov, and multiple ontologies (SNOMED CT, UBERON, and MeSH). We employed entity recognition, relation extraction, and ontology mapping to establish AcuKG, with human-in-the-loop to ensure data quality. Two cases evaluated AcuKG\u2019s usability: (1) how AcuKG advances acupuncture research for obesity and (2) how AcuKG enhances large language model (LLM) application on acupuncture question-answering.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>AcuKG comprises 1839 entities and 11\u00a0527 relations, mapped to 1836 standard concepts in 3 ontologies. Two use cases demonstrated AcuKG\u2019s effectiveness and potential in advancing acupuncture research and supporting LLM applications. In the obesity use case, AcuKG identified highly relevant acupoints (eg, ST25, ST36) and uncovered novel research insights based on evidence from clinical trials and literature. When applied to LLMs in answering acupuncture-related questions, integrating AcuKG with GPT-4o and LLaMA 3 significantly improved accuracy (GPT-4o: 46% \u2192 54%, P\u2009=\u2009.03; LLaMA 3: 17% \u2192 28%, P\u2009=\u2009.01).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AcuKG is an open dataset that provides a structured and computational framework for acupuncture applications, bridging traditional practices with acupuncture research and cutting-edge LLM technologies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocaf179","type":"journal-article","created":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T11:56:34Z","timestamp":1758974194000},"page":"359-370","source":"Crossref","is-referenced-by-count":7,"title":["AcuKG: a comprehensive knowledge graph for medical 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100010,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianfu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Informatics, Mayo Clinic , Jacksonville, FL 32224,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donghong","family":"Pei","sequence":"additional","affiliation":[{"name":"The University of Texas MD Anderson Cancer Center , Houston, TX 77030,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qin","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Science Library, Chinese Academy of Sciences , Beijing 100190,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiwei","family":"Lu","sequence":"additional","affiliation":[{"name":"Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences , Beijing 100010,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2413-5918","authenticated-orcid":false,"given":"Yan","family":"Hu","sequence":"additional","affiliation":[{"name":"McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston , Houston, TX 77030,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8865-7717","authenticated-orcid":false,"given":"Fang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Informatics, Mayo Clinic , Jacksonville, FL 32224,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Medicine, Harvard Medical School , Boston, MA 02115,","place":["United States"]},{"name":"Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women\u2019s Hospital , Boston, MA 02115,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongqun","family":"He","sequence":"additional","affiliation":[{"name":"Unit for Laboratory Animal Medicine, Center for Computational Medicine and Bioinformatics, Department of Learning Health Science, University of Michigan Medical School , Ann Arbor, MI 48109,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cui","family":"Tao","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Informatics, Mayo Clinic , Jacksonville, FL 32224,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5274-4672","authenticated-orcid":false,"given":"Hua","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics and Data Science, School of 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