《中国工程科学》 >> 2023年 第25卷 第2期 doi: 10.15302/J-SSCAE-2023.07.005
面向智慧税务的大数据知识工程技术及应用
1. 西安交通大学,西安710049;
2. 陕西省大数据知识工程重点实验室,西安710049;
3. 西安交通大学计算机科学与技术学院,西安710049;
4. 西安交通大学继续教育学院,西安710049
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摘要
税收在国家治理中发挥着基础性、支撑性作用,实现智慧税务是政府在数字时代转型的必然要求,因而梳理智慧税务中的关键问题并探讨发展思路兼具理论研究与实践应用价值。本文分析了我国智慧税务领域的发展现状及面临挑战,提出了以“数据知识化、知识体系化、知识可推理”为核心的大数据知识工程解决方案,构建了由知识源层、知识提取层、知识图谱层、知识推理层、应用层组成的“五层”技术架构;结合大数据知识工程在智慧税务领域中的代表性应用案例,如知识驱动的税收优惠计算、可解释的税收风险识别、税收政策智能化决策支持、智慧问税,探讨了所提方案的局限性并论述了进一步的研究方向。从数据、技术、生态三方面出发,形成了规范涉税数据、健全国家数据共享/ 开放/ 保障体系,融合并更新信息学科成果,完善面向智慧税务的大数据知识工程应用系统,推动大数据知识工程技术的标准建设与人才培养等发展建议,以期为基于大数据知识工程的智慧税务高质量发展研究提供参考。
参考文献
[ 1 ] Schreiber G, Akkermans H, Anjewierden A. Knowledge engineering and management: The common KADS methodology [M]. Cambridge: MIT Press, 2000.
[ 2 ] Wu X D, Chen H H, Wu G Q, al et. Knowledge engineering with big data [J]. IEEE Intelligent Systems, 2015, 30(5): 46‒55.
[ 3 ]
吴信东 , 靳小龙 , 陈欢欢 . 大数据知识工程研究进展与发展趋势 [J]. 中国计算机学会通讯 , 2021 , 17 6 : 1 ‒ 8 .
Wu X D , Jin X L , Chen H H . Research progress and development trend of big data knowledge engineering [J]. Communications of the CCF , 2021 , 17 6 : 1 ‒ 8
[ 4 ]
郑庆华 , 刘均 , 魏笔凡 , 等 . 知识森林: 理论、方法与实践 [M]. 北京 : 科学出版社 , 2021 .
Zheng Q H , Liu J , Wei B F , al e t . Knowledge forest: Theory, methodology, and application [M]. Beijing : Science Press , 2021 .
[ 5 ] Manyika J, Chui M, Bughin J, al et. Disruptive technologies: Advances that will transform life, business, and the global economy [M]. San Francisco: McKinsey Global Institute, 2013.
[ 6 ] Adams T. Google and the future of search: Amit singhal and the knowledge graph [J]. The Guardian, 2013, 19: 1‒8.
[ 7 ] Wang Z Y, Huang J M , Li H S, al et. Probase: A universal knowledge base for semantic search [R]. Beijing: Microsoft Research Asia, 2010.
[ 8 ] Mitchell T, Cohen W, Hruschka E, al et. Never-ending learning [J]. Communications of the ACM, 2018, 61(5): 103‒115.
[ 9 ] Lu R Q, Jia C Y, Zhang S F, al et. An exact data mining method for finding center strings and all their instances [J]. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(4): 509‒522.
[10] Tang J, Zhang J, Zhang D, al et. ArnetMiner: An expertise oriented search system for web community [EB/OL]. (2007-06-15)[2022-05-15]. https://ceur-ws.org/Vol-295/paper01.pdf. 链接1
[11] Wei B F, Liu J, Ma J, al et. Motif-based hyponym relation extraction from Wikipedia hyperlinks [J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(10): 2507‒2519.
[12]
游家兴 , 柳颖 , 杨莎莉 . 智慧税务助力高质量发展的实践与探索 [J]. 税务研究 , 2022 7 : 64 ‒ 69 .
You J X , Liu Y , Yang S L . Practice and exploration of intelligent taxation to help high-quality development [J]. Taxation Research , 2022 7 : 64 ‒ 69 .
[13]
张靖 . 深化数字技术运用 推动智慧税务建设 [J]. 税务研究 , 2022 5 : 128 ‒ 130 .
Zhang J . Deepen the application of digital technology and promote the construction of smart taxation [J]. Taxation Research , 2022 5 : 128 ‒ 130 .
[14]
孙存一 , 谭荣华 . 简析大数据支撑下的"互联网+智慧税务 " [J]. 税务研究 , 2018 4 : 104 ‒ 107 .
Sun C Y , Tan R H . Brief analysis of "Internet + intelligent taxation" supported by big data [J]. Taxation Research , 2018 4 : 104 ‒ 107 .
[15]
余红艳 , 孙丽 , 刘亚利 . 减税政策: 动因追溯、制度约束与路向选择 [J]. 税务研究 , 2022 7 : 32 ‒ 37 .
Yu H Y , Sun L , Liu Y L . Tax reduction policy: Motive tracing, institutional constraint and direction choice [J]. Tax Research , 2022 7 : 32 ‒ 37 .
[16] L Ackoff R. From data to wisdom [J]. Journal of Applied Systems Analysis, 1989, 16(1): 3‒9.
[17] Shaw J, Rudzicz F, Jamieson T, al et. Artificial intelligence and the implementation challenge [J]. Journal of Medical Internet Research, 2019, 21(7): 1‒12.
[18]
丁梦远 , 兰旭光 , 彭茹 , 等 . 机器推理的进展与展望 [J]. 模式识别与人工智能 , 2021 , 34 1 : 1 ‒ 13 .
Ding M Y , Lan X G , Peng R , al e t . Progress and prospect of machine reasoning [J]. Pattern Recognition and Artificial Intelligence , 2021 , 34 1 : 1 ‒ 13 .
[19]
张钹 , 朱军 , 苏航 . 迈向第三代人工智能 [J]. 中国科学: 信息科学 , 2020 , 50 9 : 1281 ‒ 1302 .
Zhang B , Zhu J , Su H . Towards the third generation of artificial intelligence [J]. Scientia Sinica Information , 2020 , 50 9 : 1281 ‒ 1302 .
[20] Marcus G. The next decade in AI: Four steps towards robust artificial intelligence [EB/OL]. (2020-02-14)[2022-06-15]. https://arxiv.org/abs/2002.06177. 链接1
[21] LeCun Y, Bengio Y, Hinton G. Deep learning [J]. Nature, 2015, 521(7553): 436‒444.
[22]
姜磊 , 刘琦 , 赵肄江 , 等 . 面向知识图谱的信息抽取技术综述 [J]. 计算机系统应用 , 2022 , 31 7 : 46 ‒ 54 .
Jiang L , Liu Q , Zhao Y J , al e t . Review on information extraction techniques for knowledge graph [J]. Computer Systems Applications , 2022 , 31 7 : 46 ‒ 54 .
[23] Li J, Sun A X, Han J L, al et. A survey on deep learning for named entity recognition [J]. IEEE Transactions on Knowledge and Data Engineering, 2020, 34(1): 50‒70.
[24]
程华龄 , 陈艳平 , 杨卫哲 , 等 . 基于多维语义映射的关系抽取方法研究 [J]. 计算机科学 , 2022 , 49 11 : 206 ‒ 211 .
Cheng H L , Chen Y P , Yang W Z , et al . Relation extraction based on multidimensional demantic mapping [J]. Computer Science , 2022 , 49 11 : 206 ‒ 211 .
[25] Feng J, Huang M L, Zhao L, al et. Reinforcement learning for relation classification from noisy data [C]. New Orleans: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence, 2018.
[26] Cohen W W, Ravikumar P, Fienberg S. A comparison of string metrics for matching names and records [C]. Austin: International Conference on Knowledge Discovery and Data Mining, 2003.
[27] Nguyen H L, Vu D T, Jung J J. Knowledge graph fusion for smart systems: A survey [J]. Information Fusion, 2020, 61: 56‒70.
[28] Althoff T, Dong X L, Murphy K, al et. Timemachine: Timeline generation for knowledge-base entities [C]. Sydney: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015.
[29] Vrandecic D, Krotzsch M. Wikidata: A free collaborative knowledgebase [J]. Communications of the ACM, 2014, 57(10): 78‒85.
[30] Guo L, Wu J, Li J H. Complexity at mesoscales: A common challenge in developing artificial intelligence [J]. Engineering, 2019, 5(5): 924‒929.
[31]
王军 . 以深入开展"学查改"专项工作为契机 扎实推动习近平经济思想在税务系统落地生根 [J]. 中国税务 , 2022 7 : 7 ‒ 9 .
Wang J . Taking the opportunity of the special work of "learning, investigation and reform" to promote Xi Jinping´s economic thought in the taxation system [J]. China Taxation , 2022 7 : 7 ‒ 9 .