{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T06:07:44Z","timestamp":1783577264784,"version":"3.55.0"},"reference-count":28,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:00:00Z","timestamp":1774396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>When LLMs are applied in the veterinary field, they often produce serious hallucinations and logical restrictions, especially in the accurate diagnosis of bovine disease, where accuracy is crucial. To meet this challenge, this paper proposes GraphRAG-Vet, a Knowledge Graph Retrieval-Augmented Generation framework specifically designed for the dairy industry. First, we constructed a domain knowledge map comprising 2500 elements and 3000 relationships, covering high-frequency diseases in cows such as mastitis and ketosis. Second, the semantic-to-password parsing module is designed to retrieve disease symptom subgraphs from the Neo4j database accurately. Finally, the hard constraint injection mechanism is introduced to force LLMs to generate diagnoses strictly in accordance with the retrieved graph context, thereby implementing the \u201crefuse to answer\u201d function for foreign queries. The experimental results showed that GraphRAG-Vet achieved 100% accuracy in diagnosing core infectious diseases and had an almost-zero hallucination rate compared with baseline LLMs. This study provides a reliable, low-resource solution for automated veterinary consultation.<\/jats:p>","DOI":"10.3390\/computers15040203","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T15:05:33Z","timestamp":1774451133000},"page":"203","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["GraphRAG-Vet: A Knowledge Graph-Augmented Large Language Model for Precision Bovine Disease Diagnosis"],"prefix":"10.3390","volume":"15","author":[{"given":"Licheng","family":"Qu","sequence":"first","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cunjin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanghui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,25]]},"reference":[{"key":"ref_1","unstructured":"United Nations, Department of Economic and Social Affairs, and Population Division (2022). 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