计算机与数字工程2024，Vol.52Issue(6)：1746-1753,1847,9.DOI:10.3969/j.issn.1672-9722.2024.06.026
基于深度学习的畜牧业知识图谱构建
Construction of Knowledge Graph of Animal Husbandry Based on Deep Learning
摘要
Abstract
Aiming at the problems of highly specialized knowledge and difficult sharing in the field of animal husbandry,a knowledge graph of animal husbandry involving animal husbandry species,veterinary diseases,and veterinary drugs is constructed through four steps of data acquisition,ontology construction,knowledge extraction,and knowledge storage,which reduces the ap-plication threshold.Firstly,based on the 33 types of livestock breeds in the National Inventory of Livestock and Poultry Genetic Re-sources,animal husbandry species,veterinary diseases are collected,veterinary drugs from data sources such as the census infor-mation system of livestock and poultry genetic resources,the national veterinary drug basic database,and veterinary monographs.Secondly,the conceptual architecture of animal husbandry knowledge is defined,and the domain ontology of animal husbandry is constructed.Then deep learning-based methods and rule-based methods are used to extract entities and relationships in semi-struc-tured data and unstructured data,with a total of 6 138 entities and 27 870 triples.Finally,the extracted knowledge graph triplet da-ta is saved to the Neo4j graph database,which provides knowledge base support for subsequent applications such as intelligent medi-cal care and intelligent question answering.关键词
知识图谱/深度学习/畜牧业Key words
knowledge graph/deep learning/animal husbandry分类
信息技术与安全科学引用本文复制引用
戴高阳,孟小艳,张容祯..基于深度学习的畜牧业知识图谱构建[J].计算机与数字工程,2024,52(6):1746-1753,1847,9.基金项目
新疆维吾尔自治区自然科学基金项目(编号:2019D01A50) （编号:2019D01A50）
新疆维吾尔自治区重点研发项目(编号:2017B01006-1)资助. （编号:2017B01006-1）
