[Objective] This paper builds knowledge graph for business environment to improve the utilization of resources, aiming to discover the internal entity relationship of development factors, and analyze government decision-making. [Methods] We constructed the knowledge graph based on business environment policy of Beijing, and proposed a knowledge extraction method integrating dependency syntax analysis and semantic role annotation. Then, we constructed a combined classifier to identify entity relationship triples, calculate semantic similarity, as well as perform relationship name fusion and alignment. We also designed an experiment to explore the performance of trans R model in different link prediction tasks. Finally, we identified the main influencing factors and used adjustment strategies to complete knowledge reasoning. [Results] The newly constructed knowledge graph contains 31,955 entities, 1,847 relationships and 45,682 triples. The data was stored and visualized with Neo4j and Gephi, which also supported knowledge query using cypher statement. [Limitations] Due to the complex context information, more research is needed to build a model for unclear entities to improve the performance of knowledge extraction and the quality of knowledge graph triples. [Conclusions] Our new knowledge graph could help to build an effective Q&A system, and improve the government decision-making to optimize business environment.
Bollacker K, Evans C, Paritosh P, et al. Freebase: A Collaboratively Created Graph Database for Structuring Human Knowledge[C]//Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data. 2008: 1247-1250.
[2]
Etzioni O, Cafarella M, Downey D, et al. Web-Scale Information Extraction in Knowitall: (Preliminary Results)[C]//Proceedings of the 13th International Conference on World Wide Web. 2004: 100-110.
[3]
Suchanek F M, Kasneci G, Weikum G. Yago: A Core of Semantic Knowledge[C]//Proceedings of the 16th International Conference on World Wide Web. 2007: 697-706.
[4]
Auer S, Bizer C, Kobilarov G, et al. DBpedia: A Nucleus for a Web of Open Data[C]//Proceedings of the 6th International Semantic Web Conference. 2007: 722-735.
[5]
Carlson A, Betteridge J, Kisiel B, et al. Toward an Architecture for Never-Ending Language Learning[C]//Proceedings of the 24th AAAI Conference on Artificial Intelligence. 2010:1306-1313.
[6]
Dong X, Gabrilovich E, Heitz G, et al. Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2014: 601-610.
( Xu Zhihong, Yu Ziqi, Dong Yongfeng, et al. Research on Constructing the Knowledge Graph Based on Emotional Analysis of Film Review[J]. Computer Simulation, 2020, 37(8):424-430.)
( Ouyang Jian, Liang Zhufang, Ren Shuhuai. Research on the Construction of Knowledge Graph of Large-Scale Chinese Ancient Books[J]. Library and Information Service, 2021, 65(5):126-135.)
( Liu Peng, Ye Shuai, Shu Ya, et al. Coalmine Safety: Knowledge Graph Construction and Its QA Approach[J]. Journal of Chinese Information Processing, 2020, 34(11):49-59.)
[10]
Shen G W, Wang W L, Mu Q L, et al. Data-Driven Cybersecurity Knowledge Graph Construction for Industrial Control System Security[J]. Wireless Communications and Mobile Computing, 2020: 8883696.
[11]
Fang W L, Ma L, Love P E D, et al. Knowledge Graph for Identifying Hazards on Construction Sites: Integrating Computer Vision with Ontology[J]. Automation in Construction, 2020, 119:103310.
doi: 10.1016/j.autcon.2020.103310
[12]
Huang H C, Hong Z, Zhou H M, et al. Knowledge Graph Construction and Application of Power Grid Equipment[J]. Mathematical Problems in Engineering, 2020: 8269082.
( Liao Kaiji, Huang Qiongying, Xi Yunjiang. Knowledge Graph Construction of Online Medical Community Q&A Texts[J]. Information Science, 2021, 39(3):51-59.)
[14]
Rotmensch M, Halpern Y, Tlimat A, et al. Learning a Health Knowledge Graph from Electronic Medical Records[J]. Scientific Reports, 2017, 7:5994.
doi: 10.1038/s41598-017-05778-z
pmid: 28729710
[15]
Wang L, Xie H M, Han W T, et al. Construction of a Knowledge Graph for Diabetes Complications from Expert-Reviewed Clinical Evidences[J]. Computer Assisted Surgery (Abingdon), 2020, 25(1):29-35.
[16]
Xiu X L, Qian Q, Wu S Z. Construction of a Digestive System Tumor Knowledge Graph Based on Chinese Electronic Medical Records: Development and Usability Study[J]. JMIR Medical Informatics, 2020, 8(10):e18287.
doi: 10.2196/18287
( Xiang Junyi, Hu Huijun, Liu Yu, et al. Construction of COVID-19 Supplies Knowledge Graph[J]. Journal of Wuhan University (Natural Science Edition), 2020, 66(5):409-417.)
( Du Zhiqiang, Li Yu, Zhang Yeting, et al. Knowledge Graph Construction Method on Natural Disaster Emergency[J]. Geomatics and Information Science of Wuhan University, 2020, 45(9):1344-1355.)
[19]
Xiao Z W, Zhang C X. Construction of Meteorological Simulation Knowledge Graph Based on Deep Learning Method[J]. Sustainability, 2021, 13(3):1311.
doi: 10.3390/su13031311
( Wu Saisai, Zhou Ailian, Xie Nengfu, 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.)
[21]
Zhang Y H, Zhu J, Zhu Q, et al. The Construction of Personalized Virtual Landslide Disaster Environments Based on Knowledge Graphs and Deep Neural Networks[J]. International Journal of Digital Earth, 2020, 13(12):1637-1655.
doi: 10.1080/17538947.2020.1773950
( Shao Qi, Mu Dongmei, Wang Ping, et al. Identifying Subjects of Online Opinion from Public Health Emergencies[J]. Data Analysis and Knowledge Discovery, 2020, 4(9):68-80.)
( Lv Huakui, Hong Liang, Ma Feicheng. Constructing Knowledge Graph for Financial Equities[J]. Data Analysis and Knowledge Discovery, 2020, 4(5):27-37.)
( Chen Jinghao, Zeng Zhen, Li Gang. A Question Answering System for “the Belt and Road” Investment Based on Knowledge Graph[J]. Library and Information Service, 2020, 64(12):95-105.)
( Wang Fei, Liu Jingping, Liu Bin, et al. Survey on Construction of Code Knowledge Graph and Intelligent Software Development[J]. Journal of Software, 2020, 31(1):47-66.)
( Gao Chenxiang, Huang Xinrong. Knowledge Graph Construction and Visualization of Regional Government Microblog[J]. Journal of Modern Information, 2020, 40(12):90-99.)
[28]
Al-Khatib K, Hou Y F, Wachsmuth H, et al. End-to-End Argumentation Knowledge Graph Construction[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2020, 34(5):7367-7374.
[29]
Nie Z W, Liu Y J, Yang L Y, et al. Construction and Application of Materials Knowledge Graph Based on Author Disambiguation: Revisiting the Evolution of LiFePO4[J]. Advanced Energy Materials, 2021, 11(16):2003580.
doi: 10.1002/aenm.202003580
[30]
Buscaldi D, Dessì D, Motta E, et al. Mining Scholarly Publications for Scientific Knowledge Graph Construction[A]//Hitzler P, Kirrane S, Hartig O, et al. The Semantic Web: ESWC 2019 Satellite Events[M]. 2019: 8-12.
( Wang Yufei, Zhang Ruijia, Wang Guanghui. Business Environment, “Five Connectivity” Cooperation and Economic Growth of Asian and European Countries[J]. Chinese Public Administration, 2020(9):114-120.)
[32]
Bétila R R. The Impact of Ease of doing Business on Economic Growth: A Dynamic Panel Analysis for African Countries[J]. SN Business & Economics, 2021, 1(10):1-34.
( Xu Zhongyuan, Fan Qinning. Regional Characteristics, Reasons of Differences and Optimization Countermeasures of a Law-Based Business Environment[J]. Wuhan University Journal (Philosophy & Social Science), 2021, 74(4):149-160.)
( Dong Xueqin. Research Hotspots and Trends of Business Environment Based on Scientific Knowledge Graph[J]. Modern Business Trade Industry, 2021, 42(21):24-25.)
( Wan Chao, Kong Kai. Path of Optimizing Business Environment—Perspective Based on Knowledge Map Analysis[J]. Journal of Shenyang University (Social Science), 2021, 23(2):172-178.)
( Zhang Qin, Sun Changping. Knowledge Graph Analysis of China’s Business Environment Research Priorities and Trends Based on CiteSpace[J]. Statistics and Management, 2021, 36(11):124-128.)
[37]
Joulin A, Grave E, Bojanowski P, et al. Bag of Tricks for Efficient Text Classification[C]//Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. 2017: 427-431.
( Qin Xiaohui, Hou Xia, Zhao Xue. An Entity Relation Extraction Algorithm Based on Semantic Roles Labeling and Dependency Parsing[J]. Journal of Beijing Information Science & Technology University, 2019, 34(1):64-67.)
( Wang Jiahui, Xia Zhijie, Wang Yiming, et al. Visualization and Evolution of Hot Topics of Internet Public Opinion Based on Syntax Rules and Social Network Analysis[J]. Information Science, 2020, 38(7):132-139.)
[40]
Lin Y, Liu Z, Sun M, et al. Learning Entity and Relation Embeddings for Knowledge Graph Completion[C]// Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015:2181-2187.
( Cheng Kaiyuan, Yao Junping, Li Xiaojun, et al. Recommendation Based on Knowledge Graph in Temporal Networks: Key Technologies and Progress[J]. Journal of China Academy of Electronics and Information Technology, 2021, 16(2):174-183.)
( Yu Chuanming, Zhang Zhengang, Kong Lingge. Comparing Knowledge Graph Representation Models for Link Prediction[J]. Data Analysis and Knowledge Discovery, 2021, 5(11):29-44.)