摘要
[目的]我国苹果产业发展历史悠久,几千年来积累的数据已经形成了一个"资源仓库"。由于这些数据资源之间缺乏深度的组织和语义关联,难以提供精品化的知识服务,因此,解决"资源仓库"中海量数据的知识提取,是产业数据分析的关键。[方法]文章从苹果产业的知识来源、知识获取、知识融合和知识表达4个方面对我国苹果产业知识图谱架构进行设计,提出面向苹果产业数据关联的语义模型构建思路。[结果]基于苹果产业本体的构建和知识关联法则,可以提炼和挖掘苹果产业隐含知识间的关联关系,深度融合苹果产业的数据,从知识检索、知识发现、产业指导等方面为苹果产业的发展提供知识服务。[结论]知识图谱的构建可以为苹果产业的知识组织、有效表达及深度推理提供理论框架,解决产业发展和科学研究中的相关问题,助力苹果产业的科技创新,提升我国苹果产业的竞争力。
The development of apple industry in China has a long history and the " data warehouse" has been formed with data accumulation for thousands of years. Due to the lack of deep organization and semantic association, it is difficult to provide high - quality knowledge services. Therefore, knowledge extraction of massive data in "resource warehouse" is the key to industrial data analysis. This paper designed the knowledge structure of apple industry in China from four aspects: knowledge source, knowledge acquisition, knowledge fusion and knowledge expression of apple industry, and put forward the idea of constructing semantic model for apple industry data association. The results showed that based on ontology construction of apple and association rules of knowledge, the implicit relationships between knowledge could be excavated and knowledge service also could be provided for the devel- opment of apple industry from the aspects of knowledge retrieval, knowledge discovery and industry guidance. Knowledge graph could provide theoretical framework for knowledge organization, and boosts technological innovations in apple industry as well as resolve the problems of industry development and scientific research. Through the construction of apple knowledge graph, technological innovation and the competitiveness of apple industry can be enhanced in China.
出处
《中国农业资源与区划》
CSCD
北大核心
2017年第11期40-45,共6页
Chinese Journal of Agricultural Resources and Regional Planning
基金
中国农业科学院科技创新工程"语义知识发现系统建设与应用"(CAAS-ASTIP-2017-AII)
关键词
苹果产业
知识图谱
语义关联
本体
构建
apple industry
knowledge graph
semantic association
ontology
construction