摘要
[目的/意义]随着农业知识图谱数据规模的增长,图谱的节点和关系复杂度不断提升,这对其训练和表示提出了新的挑战。在此背景下,探索如何在保全知识图谱结构的同时降低资源消耗并加快嵌入速度具有重要的研究和应用意义。[方法/过程]针对这一问题,本研究提出了一种基于HARP框架的农业知识图谱层次表示模型。该模型利用农业知识图谱的层次性特征,采用一种改进的基于关系路径随机行走策略,有效地保留了图谱中节点的层次性和非对称关系结构。[结果/结论]1)与HARP框架相比,使用LEIDEN的HRWP模型能更好地保留空间结构,并快速收敛了速度;2)采用HRWP的融合模型训练时间基本小于二者训练时间总和,且对原算法时间复杂度影响较小;3)结合HRWP的传统算法各指标平均提高2%,非神经网络模型有显著提升。综上,认为模型可以准确表示农业知识图谱并有效缩短训练时间。
[Purpose/Significance]In the era of big data,the volume of data is growing at an exponential rate.One of the most prominent areas affected by this growth is the field of agriculture.The use of agricultural knowledge graphs,which serve as key infrastructures for managing agricultural knowledge,has expanded significantly.However,as the number of nodes and relationships within these graphs increase,so too does their complexity.This complexity gives rise to new challenges in training and representing such large-scale knowledge graphs.It is therefore of great significance to investigate methods for speeding up the embedding process of agricultural knowledge graphs,while preserving their structural integrity and minimizing resource consumption.This research embarks on a novel exploration to address this issue.It stands out from previous studies by concentrating on a hierarchical representation model for agricultural knowledge graphs.The potential impacts of this research on propelling the advancement of the field and on addressing significant real-world problems are substantial.[Method/Process]To confront this challenge,we propose a hierarchical representation model for agricultural knowledge graphs rooted in the HARP framework.Our model leverages the inherent hierarchical features of the agricultural knowledge graph.It incorporates an improved random walk strategy based on relational paths to semantically model relationship objects within the agricultural knowledge graph.This innovative approach effectively retains the hierarchy and asymmetrical relationship structure of the nodes in the graph,setting our work apart from previous research.The validity of our proposed model is fortified by a strong foundation of theoretical and empirical evidence.[Results/Conclusions]Our experimental results reveal several key findings.First,the hierarchical random walk with path(HRWP)model using the LEIDEN algorithm can preserve the spatial structure more effectively and converge more quickly to the maximum modularity,in comparison to
作者
陈彩铭
冯建中
白林燕
王剑
谢能付
邹军
CHEN Caiming;FENG Jianzhong;BAI Linyan;WANG Jian;XIE Nengfu;ZOU Jun(Agricultural Information Institute of CAAS,Beijing 100081;Institute of Remote Sensing and Digital Earth,Chinese Academy of Sciences,Beijing 100094;International Research Center of Big Data for Sustainable Development Goals,Beijing 100094)
出处
《农业图书情报学报》
2023年第8期66-77,共12页
Journal of Library and Information Science in Agriculture
基金
国家科技创新2030新一代人工智能重大项目课题“农业智能知识服务平台”(2021ZD0113702-02)
新疆生产建设兵团(重点领域)科技攻关计划项目“昆玉市‘互联网+’的智慧农业集成示范应用技术研究”(2019AB002)
中国农业科学院科技创新工程项目(CAAS-ASTIP-2023-AIl)。
关键词
知识图谱
随机游走
表示学习
HARP框架
knowledge graph
random walk
representation learning
the hierarchical random walk with path(HRWP)framework
