期刊文献+

长江流域取水许可知识图谱问答系统 被引量:1

Knowledge graph Q&A system of water intake permission based on pre-trained language model in Changjiang River Basin
下载PDF
导出
摘要 随着水资源取水许可领域管理要求的不断提高,传统水资源取水许可信息管理系统难以满足复杂的信息检索需求,制约了水资源精细化管理水平的提升。为了打破系统间信息孤岛,提升取水许可信息检索效率,建立了长江流域取水许可知识图谱,基于大规模预训练语言模型提出了包含实体提及识别、实体链接、关系匹配等功能的知识图谱问答流水线方法,结合取水许可领域数据特点采用BM25算法进行候选实体排序,构建了长江流域取水许可知识图谱问答系统,并基于BS架构开发了Web客户端。实验表明:该系统在测试集上达到了90.37%的准确率,可支撑长江流域取水许可领域检索需求。 With the continuous increase of management requirements in the field of water intake permission,the traditional information management system of water intake permission is difficult to meet the complex information retrieval needs,which restricts the improvement of meticulous management in water resources.A knowledge graph of water intake permission in the Changjiang River Basin is established to break the information silo between systems and improve the efficiency of information retrieval in water intake permission,and a knowledge graph Q&A including entity mention recognition,entity link,relational matching and other functions is proposed based on a large-scale pre-trained language model.According to the characteristics of data in water intake permission domain,BM25 algorithm is used to sort candidate entities to construct a knowledge base question answering system in the Changjiang River Basin,and a Web client is developed based on BS framework.The experiment shows that the system achieves an accuracy rate of 90.37%on the test set,which can support the retrieval needs in the field of water intake permission in the Changjiang River Basin.
作者 曾德晶 张军 曹卫华 管党根 许婧 黎育朋 ZENG Dejing;ZHANG Jun;CAO Weihua;GUAN Danggen;XU Jin;LI Yupeng(Network and Information Center,Changjiang Water Resources Commission,Wuhan 430010,China;Smart Yangtze River Innovation Team of Changjiang Water Resources Commission,Wuhan 430010,China;Technology Innovation Center of Digital Enablement for River Basin Management,Changjiang Water Resources Commission,Wuhan 430010,China;School of Automation,China University of Geosciences,Wuhan 430074,China;Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems,Wuhan 430074,China;Engineering Research Center of Intelligent Technology for Geo-Exploration of Ministry of Education,Wuhan 430074,China)
出处 《人民长江》 北大核心 2024年第6期234-239,共6页 Yangtze River
基金 湖北省自然科学基金创新群体项目(2020CFA031)。
关键词 取水许可 知识图谱 预训练语言模型 问答系统 水资源 长江流域 water intake permission knowledge graph pre-trained language model question answering system water resources Changjiang River Basin
  • 相关文献

参考文献14

二级参考文献101

  • 1冯钧,唐志贤,朱跃龙,韦冕,卞一路,史涯晴.水利信息资源目录服务元数据定义研究[J].水利信息化,2011(S1):19-22. 被引量:6
  • 2何海芸,袁春风.基于Ontology的领域知识构建技术综述[J].计算机应用研究,2005,22(3):14-18. 被引量:41
  • 3Hachey B, Radford W, Nothman J, Honnibal M, Curran JR. Evaluating entity linking with Wikipedia. Artificial Intelligence, 2013,194:130-150.
  • 4Rau LF. Extracting company names from text. In: Proc. of the 7th IEEE Conf. on Artificial Intelligence Application. IEEE Press, 1991.29-32. [doi: 10.1109/caia.1991.120841].
  • 5Shen W, Wang JY, Luo P, Wang M. Linden: Linking named entities with knowledge base via semantic knowledge. In: Proc. of the 21st Int'l Conf. on World Wide Web. ACM Press, 2012.449-458. [doi: 10.1145/2187836.2187898].
  • 6Milne D, Witten IH. Learning to link with Wikipedia. In: Proc. of the 17th ACM Conf. on Information and Knowledge Management. ACM Press, 2008. 509-518. [doi: 10.1145/1458082.1458150].
  • 7Bunescu RC, Pasca M. Using encyclopedic knowledge for named entity disambiguation. In: Proc. of the 7th Conf. of the European Chapter of the Association for Computational Linguistics. ACM Press, 2006.9-16.
  • 8Milosavljevic M, Delort JY, Hachey B, Arunasalam B, Radford W, Curran JR. Automating financial surveillance. In: Proc. of the User Centric Media. Berlin, Heidelberg: Springer-Verlag, 2010 305-311. [doi: 10.1007/978-3-642-12630-7_38].
  • 9Zheng J, Mao YH. Word sense tagging method based. Journal of Tsinghua University (Sci. & Tech.), 2001,41(3):117-120 (in Chinese with English abstract).
  • 10Guo Y, Che W, Liu T, Li S. A graph-based method for entity linking. In: Proc. of the 5th Int'l Joint Conf. on Natural Language Processing. 2011. 1010-1018.

共引文献233

同被引文献11

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部