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Strategic Study of CAE >> 2023, Volume 25, Issue 2 doi: 10.15302/J-SSCAE-2023.07.005

Technologies and Applications of Big Data Knowledge Engineering for Smart Taxation Systems

1. Xi’an Jiaotong University, Xi'an 710049, China;

2. Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering,Xi'an 710049, China;

3. School of Computer Science and Technology, Xi’an Jiaotong University, Xi'an 710049, China;

4. School of Continuing Education, Xi’an Jiaotong University, Xi'an 710049, China

Funding project:National Natural Science Foundation of China (62250009, 61721002); China Engineering Science and Technology Knowledge Center Project (CKCEST-2022-1-40) Received: 2022-07-30 Revised: 2022-09-29 Available online: 2022-12-08

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Abstract

Taxation is vital for national governance, and the digital transformation of governments necessitates smart taxation. Therefore, analyzing the key issues and exploring the development ideas for smart taxation is of both theoretical and practical values. In this study, following an analysis of the development status and challenges facing China’s intelligent taxation field, we proposed a big data knowledge engineering approach that emphasizes data knowledgeization, knowledge systematization, and knowledge reasonability, and developed a five-layer technical architecture that consists of knowledge sources, knowledge extraction, knowledge mapping, knowledge reasoning, and application layers. After elaborating the representative application scenarios including knowledge-driven tax preference calculation, interpretable tax risk identification, intelligent decision support for tax policies, and smart tax questioning,we investigated the limitations of the proposed approach and further discussed the directions for future research. Furthermore, we proposed the following development suggestions in terms of data, technology, and ecology: (1) standardizing tax-related information and improving the national data sharing, opening, and guarantee system; (2) integrating the achievements of various information disciplines and improving the application system of big data knowledge engineering for smart taxation; and (3) promoting talent training and the development of technical standards for big data knowledge engineering.

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References

[ 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. link1

[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. link1

[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 ‍.

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