基于百科知识的军事装备知识图谱构建与应用
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摘要
为解决信息时代网络中的军事装备数据分布较为稀疏,数据间缺乏良好的关联与组织,导致知识难以被高效利用的问题,提出了一种军事装备知识图谱的构建方法,该方法通过网络爬虫不断获取原始百科数据,利用高质量的百科知识对知识图谱构建过程中的知识抽取、知识融合、知识图谱的储存与更新等关键技术进行研究,并在已构建的知识图谱基础上实现了军事装备领域的知识问答。该方法有效利用了网页中的零散军事装备数据,实现了军事装备知识图谱的构建。
In the information age,the distribution of military equipment data is sparse in the network. And there is a lack of good association and organization for military equipment data. These all lead to the difficulty of efficient use of knowledge. To solve the above problems,a method of constructing military equipment knowledge graph was proposed. This method obtains the original encyclopedia data through the web crawler,and uses these high quality encyclopedia knowledge to research the key technologies such as knowledge extraction, knowledge fusion, storage and update of knowledge graph in the process of constructing knowledge graph. On the basis of the constructed knowledge graph,knowledge questions and answers in the field of military equipment were also realized. The method effectively utilizes the scattered military equipment data in the webpage,and realizes the construction of knowledge graph of military equipment.
In the information age,the distribution of military equipment data is sparse in the network. And there is a lack of good association and organization for military equipment data. These all lead to the difficulty of efficient use of knowledge. To solve the above problems,a method of constructing military equipment knowledge graph was proposed. This method obtains the original encyclopedia data through the web crawler,and uses these high quality encyclopedia knowledge to research the key technologies such as knowledge extraction, knowledge fusion, storage and update of knowledge graph in the process of constructing knowledge graph. On the basis of the constructed knowledge graph,knowledge questions and answers in the field of military equipment were also realized. The method effectively utilizes the scattered military equipment data in the webpage,and realizes the construction of knowledge graph of military equipment.
引文
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[2]漆桂林,高桓,吴天星.知识图谱研究进展[J].情报工程,2017,3(1):4-25.
[3] PUJARA J,MIAO H,GETOOR L,et al. Knowledge graph identification[C]//International Semantic Web Conference.Springer-Verlag New York,Inc. 2013:542-557.
[4] BIZER C,LEHMANN J,KOBILAROV G,et al. DBpedia-a crystallization point for the Web of data[J]. Web Semantics Science Services&Agents on the World Wide Web,2009,7(3):154-165.
[5] SUCHANEK F M,KASNECI G,WEIKUM G. YAGO:a large ontology from wikipedia and wordnet[J]. Web Semantics Science Services&Agents on the World Wide Web,2007,6(3):203-217.
[6] WU W T,LI H S,WANG H X. Probase:a probabilistic taxonomy for text understanding[C]//Proceedings of the 2012ACM SIGMOD International Conference on Management of Data,ACM New York,USA,2012:481-492.
[7] NIU X,SUN X R,WANG H F,et al. Zhishi. me-weaving Chinese linking open data[C]//International Conference on the Semantic Web. Springer-Verlag,2011:205-220.
[8]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,2016,37(4):8-13.
[9]赵明,杜亚茹,杜会芳,等.植物领域知识图谱构建中本体非分类关系提取方法[J].农业机械学报,2016,47(9):278-284.
[10]徐增林,盛泳潘,贺丽荣,等.知识图谱技术综述[J].电子科技大学学报,2016,45(4):589-606.
[11] DONG X,GABRILOVICH E,HEITZ G,et al. Knowledge vault:a web-scale approach to probabilistic knowledge fusion[C]//Proc of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York:ACM,2014:601-610.
[12]胡芳槐.基于多种数据源的中文知识图谱构建方法研究[D].上海:华东理工大学,2015.
[13]刘峤,李杨,段宏,等.知识图谱构建技术综述[J].计算机研究与发展,2016,53(3):582-600.
[14] MIKOLOV T,CORRADO G,CHEN K,et al. Efficient estimation of word representations in vector space[C]//International Conference on Learning Representations. 2013:1-12.
[15]毛先领,李晓明.问答系统研究综述[J].计算机科学与探索,2012,6(3):193-207.
[16] ZHENG W,ZOU L,LIAN X,et al. How to build templates for RDF question/answering:an uncertain graph similarity join approach[C]//ACM SIGMOD International Conference on Management of Data. ACM,2015:1809-1824.