• Scopus
  • Chinese Science Citation Database (CSCD)
  • A Guide to the Core Journal of China
  • CSTPCD
  • F5000 Frontrunner
  • RCCSE
Advanced search
Liu Mingpeng, Wang Zhongming, Ma Wenjun. Construction and application of ontology based knowledge graph for afforestation tree species[J]. Journal of Beijing Forestry University, 2023, 45(8): 109-122. DOI: 10.12171/j.1000-1522.20210386
Citation: Liu Mingpeng, Wang Zhongming, Ma Wenjun. Construction and application of ontology based knowledge graph for afforestation tree species[J]. Journal of Beijing Forestry University, 2023, 45(8): 109-122. DOI: 10.12171/j.1000-1522.20210386
shu

Construction and application of ontology based knowledge graph for afforestation tree species

  • Research Institute of Forestry Policy and Information, Chinese Academy of Forestry, Beijing 100091, China

More Information
  • Received Date: September 27, 2021
  • Revised Date: June 20, 2022
  • Available Online: July 25, 2023
  • Published Date: August 24, 2023
    Graphical Abstract
    • Abstract
  •   Objective  With the vigorous development of big data, artificial intelligence and other information technologies, information services in various fields have been transformed from data services to knowledge services. This paper captures the problems of complicated, multi-source heterogeneous and difficult access to forestry knowledge, and through the research on construction method and application scenario of knowledge graph of afforestation tree species based on ontology, in order to provide a more effective way to organize and express knowledge in the field of afforestation.
      Method  This article takes the knowledge related to afforestation tree species as the research object, takes the actual needs of afforestation work as the research hotspot, and constructs an afforestation tree species ontology covering seven types of concepts such as afforestation tree species, diseases and pests, experts, organizations, provinces, application products and afforestation technology, 12 attributes such as tree species alias, Latin name and forest species type, and seven relationships such as distribution, application and research by discussing and exchanging with experts in afforestation field using the seven-step method. A knowledge graph of afforestation tree species was constructed based on the entities, attributes, and relationships defined in the ontology model of afforestation tree species. Relevant knowledge applications based on the knowledge graph of afforestation tree species were designed and implemented.
      Result  The research applied the knowledge graph technology to the actual scenario of afforestation tree species and completed the construction of knowledge graph, realized the knowledge Q&A and knowledge visualization based on afforestation tree species knowledge graph, and provided knowledge services for researchers and workers in afforestation field. In addition, the study proposed a map service model combining knowledge Q&A of afforestation tree species, GIS map and tree species pictures, and built a GIS map service platform of afforestation tree species to realize the problem of suitability of afforestation tree species in spatial location by “GIS map + tree species pictures + text description”.
      Conclusion  The ontology model of afforestation tree species constructed in this thesis provides the possibility of subsequent knowledge reuse and sharing. The realized knowledge graph of afforestation tree species, knowledge Q&A of afforestation tree species and GIS map service platform effectively solve the problems of multi-source, heterogeneity, low data value and difficult access of forestry knowledge, and realize the transformation of domain information resources from data service to knowledge services. The knowledge scale of the knowledge graph of afforestation tree species needs to be further expanded and more application scenarios based on the knowledge graph need to be explored in subsequent studies, with the view to providing high-quality knowledge services for forestry development and research.
    • FullText(HTML)
    • References (42)
  • [1]
    刘广平, 刘波, 滕轶. “智慧林业”时代的信息资源开发与利用探讨[J]. 林业资源管理, 2013(6): 33−36. doi: 10.3969/j.issn.1002-6622.2013.06.008

    Liu G P, Liu B, Teng Y. Information resource development and utilization in the wisdom forestry times[J]. Forest Resources Management, 2013(6): 33−36. doi: 10.3969/j.issn.1002-6622.2013.06.008
    [2]
    李晓靖. 林业信息化建设难点及解决对策探讨[J]. 现代园艺, 2018(20): 222. doi: 10.3969/j.issn.1006-4958.2018.20.189

    Li X J. Discussion on difficulties and solutions of forestry informatization construction[J]. Xiandai Horticulture, 2018(20): 222. doi: 10.3969/j.issn.1006-4958.2018.20.189
    [3]
    李毅, 彭岩, 徐林, 等. 林业资源共享服务平台建设关键技术及应用研究[J]. 中国林业产业, 2016(7): 143−144.

    Li Y, Peng Y, Xu L, et al. Research on key technology and application of forestry resource sharing service platform construction[J]. China Forestry Industry, 2016(7): 143−144.
    [4]
    漆桂林, 高桓, 吴天星. 知识图谱研究进展[J]. 情报工程, 2017(1): 4−25. doi: 10.3772/j.issn.2095-915x.2017.01.002

    Qi G L, Gao H, Wu T X. The research advances of knowledge graph[J]. Technology Intelligence Engineering, 2017(1): 4−25. doi: 10.3772/j.issn.2095-915x.2017.01.002
    [5]
    Sowa J. Principles of Semantic Networks: exploration in the representation of knowledge[J]. Frame Problem in Artificial Intelligence, 1991, 2(3): 109−136.
    [6]
    Singhal A. Official google blog: introducing the knowledge graph: things, not strings[EB/OL]. [2021−09−15]. https://www.blog.google/products/search/introducing-knowledge-graph-things-not/.
    [7]
    李涓子, 侯磊. 知识图谱研究综述[J]. 山西大学学报(自然科学版), 2017, 40(3): 454−459. doi: 10.13451/j.cnki.shanxi.univ(nat.sci.).2017.03.008

    Li J Z, Hou L. Reviews on knowledge graph research[J]. Journal of Shanxi University (Natural Science Edition), 2017, 40(3): 454−459. doi: 10.13451/j.cnki.shanxi.univ(nat.sci.).2017.03.008
    [8]
    Auer S, Bizer C, Kobilarov G, et al. DBpedia: a nucleus for a web of open data[C/OL]//The Semantic Web, 6th International Semantic Web Conference, 2nd Asian Semantic Web Conference. Berlin: Springer, 2007: 722−735[2021−10−18]. http://sites.wiwiss.fu-berlin.de/suhl/bizer/pub/LinkedDataTutorial/.
    [9]
    Niu X, Sun X R, Wang H F, et al. Zhishi. me-weaving Chinese linking open data[C]//Aroyo L. International Semantic Web Conference. Berlin: Springer, 2011: 205−220.
    [10]
    王文广, 徐永林, 贺梦洁, 等. 基于知识图谱的通用知识问答系统: 体系与方法[J]. 新一代信息技术, 2020, 3(7): 38−47.

    Wang W G, Xu Y L, He M J, et al. Knowledge graph based universal question answering system: framework and methods[J]. New Generation of Information Technology, 2020, 3(7): 38−47.
    [11]
    Frber M. The microsoft knowledge graph: a linked data source with 8 billion triples of scholarly data[M]//Ghidini C, Hartig O, Maleshkova M, et al. The semantic web-ISWC 2019. Berlin: Springer, 2019: 113−129.
    [12]
    车金立, 唐力伟, 邓士杰, 等. 基于百科知识的军事装备知识图谱构建与应用[J]. 兵器装备工程学报, 2019, 40(1): 148−153. doi: 10.11809/bqzbgcxb2019.01.031

    Che J L, Tang L W, Deng S J, et al. Construction and application of military equipment knowledge graph based on encyclopedia knowledge[J]. Journal of Ordnance Equipment Engineering, 2019, 40(1): 148−153. doi: 10.11809/bqzbgcxb2019.01.031
    [13]
    阮彤, 孙程琳, 王昊奋, 等. 中医药知识图谱构建与应用[J]. 医学信息学杂志, 2016(4): 8−13. doi: 10.3969/j.issn.1673-6036.2016.04.002

    Ruan T, Sun C L, Wang H F, et al. Construction of traditional Chinese medicine knowledge graph and its application[J]. Journal of Medical Informatics, 2016(4): 8−13. doi: 10.3969/j.issn.1673-6036.2016.04.002
    [14]
    陆伟, 戚越, 胡潇戈, 等. 图书馆自动问答系统的设计与实现[J]. 情报工程, 2019, 5(2): 5−16.

    Lu W, Qi Y, Hu X G, et al. Design and implementation of library automatic question answering system[J]. Technology Intelligence Engineering, 2019, 5(2): 5−16.
    [15]
    中国中文信息学会语言与知识计算专业委员会. 知识图谱发展报告(2018)[R]. 北京: 中国中文信息学会, 2018.

    Chinese Information Processing Society of China, Committee on Language and Knowledge Computing. Knowledge graph development report(2018)[R]. Beijing: Chinese Information Processing Society of China, 2018.
    [16]
    吴天星. 面向社交站点的双语知识图谱构建方法的研究[D]. 南京: 东南大学, 2018.

    Wu T X. Research on approaches to bilingual knowledge graph construction from social web sites[D]. Nanjing: Southeast University, 2018.
    [17]
    俞思伟, 范昊, 王菲, 等. 基于知识图谱的智能医疗研究[J]. 医疗卫生装备, 2017, 38(3): 109−111.

    Yu S W, Fan H, Wang F, et al. Research on intelligent medicine based on knowledge graph[J]. Chinese Medical Equipment Journal, 2017, 38(3): 109−111.
    [18]
    李代祎, 盛杰, 刘运星, 等. 基于知识图谱的军事武器问答系统[J]. 指挥信息系统与技术, 2020, 11(5): 58−65. doi: 10.15908/j.cnki.cist.2020.05.011

    Li D Y, Sheng J, Liu Y X, et al. Military weapon QA system based on knowledge graph[J]. Command Information System and Technology, 2020, 11(5): 58−65. doi: 10.15908/j.cnki.cist.2020.05.011
    [19]
    陈亚东, 鲜国建, 寇远涛, 等. 我国苹果产业知识图谱构建研究[J]. 中国农业资源与区划, 2017, 38(11): 40−45. doi: 10.7621/cjarrp.1005-9121.20171106

    Chen Y D, Xian G J, Kou Y T, et al. Study on construction of knowledge graph of apple industry in China[J]. Chinese Journal of Agricultural Resources and Regional Planning, 2017, 38(11): 40−45. doi: 10.7621/cjarrp.1005-9121.20171106
    [20]
    夏迎春. 基于知识图谱的农业知识服务系统研究[D]. 合肥: 安徽农业大学, 2018.

    Xia Y C. Agriculture knowledge service system based on knowledge graph[D]. Hefei: Anhui Agricultural University, 2018.
    [21]
    胡宸恺, 魏鑫, 姜国强, 等. 基于百科数据的林业知识图谱的构建与应用[J]. 智能计算机与应用, 2020, 10(10): 47−53. doi: 10.3969/j.issn.2095-2163.2020.10.012

    Hu C K, Wei X, Jiang G Q, et al. Construction and application of forestry knowledge graph based on encyclopedia data[J]. Intelligent Computer and Applications, 2020, 10(10): 47−53. doi: 10.3969/j.issn.2095-2163.2020.10.012
    [22]
    Chen Y, Kuang J, Cheng D, et al. AgriKG: an agricultural knowledge graph and its applications[M]//Orzechowska P. Complexity in polish phonotactics. Berlin: Springer, 2019.
    [23]
    杨一帆. 钢结构领域知识图谱构建研究[D]. 北京: 北京交通大学, 2020.

    Yang Y F. Research on construction of knowledge map in steel structure[D]. Beijing: Beijing Jiaotong University, 2020.
    [24]
    张梅, 郝佳, 阎艳, 等. 基于本体的知识建模技术[J]. 北京理工大学学报, 2010, 30(12): 1405−1408, 1431. doi: 10.15918/j.tbit1001-0645.2010.12.021

    Zhang M, He J, Yan Y, et al. Ontology-based knowledge modeling[J]. Transactions of Beijing Institute of Technology, 2010, 30(12): 1405−1408, 1431. doi: 10.15918/j.tbit1001-0645.2010.12.021
    [25]
    王昊奋, 漆桂林, 陈华钧, 等. 知识图谱: 方法、实践与应用[M]. 北京: 电子工业出版社, 2019.

    Wang H F, Qi G L, Chen H J, et al. Knowledge graph: method, practice and application[M]. Beijing: Publishing House of Electronics Industry, 2019.
    [26]
    郭梦莹, 周璐, 孙燕. “领域本体七步法”在中医辨证推理知识库构建中的应用[J]. 世界科学技术—中医现代化, 2019, 21(12): 2646−2651.

    Guo M Y, Zhou L, Sun Y. Application of “Domain Ontology Seven-step Method” in the knowledge base construction of TCM differentiation[J]. World Science and Technology— Modernization of Traditional Chinese Medicine, 2019, 21(12): 2646−2651.
    [27]
    吴赛赛, 周爱莲, 谢能付, 等. 基于深度学习的作物病虫害可视化知识图谱构建[J]. 农业工程学报, 2020, 36(24): 177−185. doi: 10.11975/j.issn.1002-6819.2020.24.021

    Wu S S, Zhou A L, Xie N F, et al. Construction of visualization domain-specific knowledge graph of crop diseases and pests based on deep learning[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(24): 177−185. doi: 10.11975/j.issn.1002-6819.2020.24.021
    [28]
    高学攀, 杜楚, 吴金亮. 基于BiLSTM-CRF的军事命名实体识别方法[J]. 无线电工程, 2020, 50(12): 1050−1054. doi: 10.3969/j.issn.1003-3106.2020.12.007

    Gao X P, Du C, Wu J L. Research on method of military named entity recognition based on BiLSTM-CRF[J]. Radio Engineering, 2020, 50(12): 1050−1054. doi: 10.3969/j.issn.1003-3106.2020.12.007
    [29]
    李灵芳, 杨佳琦, 李宝山, 等. 基于BERT的中文电子病历命名实体识别[J]. 内蒙古科技大学学报, 2020, 39(1): 71−77. doi: 10.16559/j.cnki.2095-2295.2020.01.015

    Li L F, Yang J Q, Li B S, et al. Named entity recognition of Chinese electronic medical record based on BERT[J]. Journal of Inner Mongolia University of Science and Technology, 2020, 39(1): 71−77. doi: 10.16559/j.cnki.2095-2295.2020.01.015
    [30]
    杨穗珠, 刘艳霞, 张凯文, 等. 远程监督关系抽取综述[J]. 计算机学报, 2021, 44(8): 1636−1660. doi: 10.11897/SP.J.1016.2021.01636

    Yang S Z, Liu Y X, Zhang K W, et al. Survey on distantly-supervised relation extraction[J]. Chinese Journal of Computers, 2021, 44(8): 1636−1660. doi: 10.11897/SP.J.1016.2021.01636
    [31]
    高源, 席耀一, 李弼程. 基于依存句法分析与分类器融合的触发词抽取方法[J]. 计算机应用研究, 2016, 33(3): 1407−1410. doi: 10.3969/j.issn.1001-3695.2016.05.029

    Gao Y, Xi Y Y, Li B C. Trigger extraction algorithm based on dependency parsing and classifier fusion[J]. Application Research of Computers, 2016, 33(3): 1407−1410. doi: 10.3969/j.issn.1001-3695.2016.05.029
    [32]
    Zhang H P, Yu H K, Xiong D Y, et al. HHMM-based Chinese lexical analyzer ICTCLAS[C]// Proceedings of the Second SIGHAN Workshop on Chinese Language Processing. Association for Computational Linguistics, 2003.
    [33]
    阮彤, 王梦婕, 王昊奋, 等. 垂直知识图谱的构建与应用研究[J]. 知识管理论坛, 2016, 3(3): 226−234. doi: 10.13266/j.issn.2095-5472.2016.027

    Ruan T, Wang M J, Wang H F, et al. Construction and application of vertical knowledge graph[J]. Knowledge Management Forum, 2016, 3(3): 226−234. doi: 10.13266/j.issn.2095-5472.2016.027
    [34]
    Kolomiyets O, Moens M F. A survey on question answering technology from an information retrieval perspective[J]. Information Sciences, 2011, 181(24): 5412−5434. doi: 10.1016/j.ins.2011.07.047
    [35]
    Gao R X, Li C. Knowledge question-answering system based on knowledge graph of traditional Chinese medicine[C]// IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). New York: IEEE, 2020.
    [36]
    Li Q, Hao Z G, Yang L P. Question answering system based on food spot-check knowledge graph[C]// ICCDE 2020: the 6th International Conference on Computing and Data Engineering. Sanya: Association for Computing Machinery, 2020: 168−172.
    [37]
    Yuan S. Question answering system based on tourism knowledge graph[J]. Journal of Physics Conference Series, 2021, 1883(1): 12064. doi: 10.1088/1742-6596/1883/1/012064
    [38]
    马晨浩. 基于甲状腺知识图谱的自动问答系统的设计与实现[J]. 智能计算机与应用, 2018, 8(3): 102−107. doi: 10.3969/j.issn.2095-2163.2018.03.024

    Ma C H. Design and implementation of automatic question answering system based on thyroid knowledge graph[J]. Intelligent Computer and Applications, 2018, 8(3): 102−107. doi: 10.3969/j.issn.2095-2163.2018.03.024
    [39]
    张沪寅, 刘道波, 温春艳. 基于《知网》的词语语义相似度改进算法研究[J]. 计算机工程, 2015, 41(2): 151−156. doi: 10.3969/j.issn.1000-3428.2015.02.029

    Zhang H Y, Liu D B, Wen C Y. Research on improved algorithm of word semantic similarity based on HowNet[J]. Computer Engineering, 2015, 41(2): 151−156. doi: 10.3969/j.issn.1000-3428.2015.02.029
    [40]
    周永梅, 陶红, 陈姣姣, 等. 自动问答系统中的句子相似度算法的研究[J]. 计算机技术与发展, 2012, 22(5): 75−78.

    Zhou Y M, Tao H, Chen J J, et al. Study on sentence similarity approach of automatic Ask & Answer system[J]. Computer Technology and Development, 2012, 22(5): 75−78.
    [41]
    欧阳林男, 陈少雄, 张维耀, 等. 柠檬桉在中国的适生地理分布及其影响因子[J]. 生态学杂志, 2019, 38(2): 361−367. doi: 10.13292/j.1000-4890.201902.005

    Ouyang L N, Chen S X, Zhang W Y, et al. The suitable geographic range for Corymbia citriodora in China and the influencing factors[J]. Chinese Journal of Ecology, 2019, 38(2): 361−367. doi: 10.13292/j.1000-4890.201902.005
    [42]
    任明兴, 袁宗珍. 造林规划设计中造林树种及密度选择分析[J]. 种子科技, 2016, 34(9): 88. doi: 10.3969/j.issn.1005-2690.2016.09.060

    Ren M X, Yuan Z Z. Selection of tree species and density in afforestation planning and design[J]. Seed Science and Technology, 2016, 34(9): 88. doi: 10.3969/j.issn.1005-2690.2016.09.060
    • Related Articles

  • Cited by

    Periodical cited type(10)

    1. 邹玉珍，曾庆伟，武红敢，郑仁高. 变色立木卫星影像样本特征分析及应用. 中国森林病虫. 2023(03): 1-8 .
    2. 焦全军，郑焰锋，黄文江，张兵，张鹤译，史宜梦，吴发云，付安民. 陆地生态系统碳监测卫星松材线虫病变色木识别指数研究. 林业资源管理. 2023(04): 123-131 .
    3. 曾庆伟，武红敢，张静，杨雅菲. 碳卫星在变色立木遥感监测中的应用潜力分析. 卫星应用. 2023(11): 20-25 .
    4. 李炜浩，张硕，刘梓航，苏旻，高浩然，刘善军. 基于光谱指数法的本溪市域红叶提取方法研究. 测绘与空间地理信息. 2022(04): 47-50 .
    5. 戴丽，周席华，罗治建，武红敢，陈亮. 湖北松材线虫病卫星遥感监管技术初探. 湖北林业科技. 2022(04): 60-64 .
    6. 孙红，曾庆伟. “高分七号”数据在松材线虫病松树样木监测中的应用. 林业科技通讯. 2022(09): 27-29 .
    7. 陶欢，李存军，程成，蒋丽雅，胡海棠. 松材线虫病变色松树遥感监测研究进展. 林业科学研究. 2020(03): 172-183 .
    8. 陶欢，李存军，谢春春，周静平，淮贺举，蒋丽雅，李凤涛. 基于HSV阈值法的无人机影像变色松树识别. 南京林业大学学报(自然科学版). 2019(03): 99-106 .
    9. 武红敢，牟晓伟，杨清钰，王成波. 无人机遥感技术在重庆市沙坪坝区松材线虫病监测中的应用. 林业资源管理. 2019(02): 109-115 .
    10. 武红敢，王成波，常原飞. 变色立木的无人机遥感监测技术. 中国森林病虫. 2019(04): 29-32+37 .

    Other cited types(4)

Catalog

    Get Citation
    PDF
    XML
    Article views (423) PDF downloads (101) Cited by(14)
    Turn off MathJax
    Article Contents
    Abstract
    References

    Copyright © 2013 《北京林业大学学报》编辑部

    Address：North-Star Times Tower, No.8 Beichendong Road, Chaoyang District, Beijing, China China Pos：100083

    Tel：010-62337673 Email：bldxed@bjfu.edu.cn 京ICP备05066833号-1

    Supported by: Beijing Renhe Information Technology Co., Ltd.  Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return