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GB/T 45923.2-2025: Artificial intelligence - Knowledge graph application platform - Part 2: Performance requirements and testing method
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GB/T 45923.2-2025: Artificial intelligence - Knowledge graph application platform - Part 2: Performance requirements and testing method

---This is an excerpt. Full copy of true-PDF in English version (including equations, symbols, images, flow-chart, tables, and figures etc.), auto-downloaded/delivered in 9 seconds, can be purchased online: https://www.ChineseStandard.net/PDF.aspx/GBT45923.2-2025
ICS 35.020 CCSL70 National Standard of the People's Republic of China Artificial Intelligence Knowledge Graph Application Platform Part 2.Performance requirements and test methods Released on June 30, 2025 Implementation on October 1, 2025 State Administration for Market Regulation The National Standardization Administration issued

Table of Contents

Preface III Introduction V 1 Scope 1 2 Normative references 1 3 Terms and Definitions 1 4 Abbreviations 2 5.Knowledge Graph Construction Performance Requirements 2 5.1 Knowledge Representation 2 5.2 Knowledge Modeling 3 5.3 Knowledge Acquisition 3 5.4 Knowledge Storage 3 5.5 Knowledge Fusion 3 5.6 Knowledge Computing 3 5.7 Knowledge Tracing 4 5.8 Knowledge Evolution 4 5.9 Knowledge Backup 4 5.10 Large Model Integration 4 6 Knowledge Graph Application Performance Requirements 4 6.1 Responsiveness 4 6.2 Portability 4 6.3 Availability 4 6.4 Maturity 5 6.5 Knowledge Application 5 7 Test Method 5 7.1 Test Environment 5 7.2 Knowledge Graph Construction Performance Testing Method 5 7.3 Knowledge Graph Application Performance Testing Method 13 Reference 17 Preface This document is in accordance with the provisions of GB/T 1.1-2020 "Guidelines for standardization work Part 1.Structure and drafting rules for standardization documents" Drafting. This document is Part 2 of GB/T 45923 "Artificial Intelligence Knowledge Graph Application Platform". Lower part. --- Part 2.Performance requirements and test methods. Some of the contents of this document may involve patents. The issuing organization of this document does not assume the responsibility for identifying patents. This document was proposed and coordinated by the National Technical Committee for Information Technology Standardization (SAC/TC28). This document was drafted by. China Electronics Standardization Institute, Shenzhen CESI Information Technology Co., Ltd., Jiangsu CESI Technology Development Co., Ltd. Co., Ltd., Shenyang Neusoft Intelligent Medical Technology Research Institute Co., Ltd., NetZhi Tianyuan Technology Group Co., Ltd., Inspur Software Technology Co., Ltd. Company, China Electronics Technology Group Corporation Big Data Research Institute Co., Ltd., Tupu Intelligent Technology (Beijing) Co., Ltd., Institute of Automation, Chinese Academy of Sciences, China Chang Three Gorges Corporation, Jiangsu Electric Power Information Technology Co., Ltd., Shenyang Institute of Automation, Chinese Academy of Sciences, Hangzhou Hikvision Digital Technology Co., Ltd., Ant Technology Group Co., Ltd., Institute of Medical Information, Chinese Academy of Medical Sciences, Institute of Biomedical Engineering, Shanghai Artificial Intelligence Research Institute Co., Ltd., Global Tone Communication Technology Co., Ltd., Dongfang Electric Corporation Dongfang Automotive Marine Machinery Co., Ltd., Beijing University of Technology, Shanghai Wenyu Information Technology Co., Ltd., Beijing Shenzhou Aerospace Software Technology Co., Ltd., Inspur Communications Information Systems Co., Ltd., Shenzhen Sih Semiconductor Co., Ltd., Shandong Yiyun Information Technology Co., Ltd., Zhejiang Chuanglin Technology Co., Ltd. Company, Xiamen Meiya Pico Information Security Research Institute Co., Ltd., Hisense Group Holdings Co., Ltd., Xiamen Yuanting Information Technology Co., Ltd. Company, Peking University, China Southern Power Grid Co., Ltd. Ultra-High Voltage Transmission Company, Holsai Technology Group Co., Ltd., Transwarp Information Technology (Shanghai) Co., Ltd., Chengdu University of Information Technology, Shanghai Jiao Tong University, Guangzhou CESI Standard Testing Institute Co., Ltd., Haiyi Zhi Information Technology (Nanjing) Co., Ltd., Guoneng Information Technology Co., Ltd., Xiamen Meiya Yian Information Technology Co., Ltd., Shenzhen UBTECH Technology Co., Ltd., State Grid Economic and Technological Research Institute Co., Ltd., Shenzhen Panyueliang Innovation Technology Co., Ltd., Datang Guoxin Binhai Offshore Xu Wenfeng, Li Jun, Zhang Yan, Meng Qingyang, Tao Xiaoying, Wei Lijun, Xu Wentao, Chen Shuyu, Lang Junqi, Zhang Mingying, Han Ning, Li Xin, Li Xiaobo, Ren Yong, Xu Jianchao, Li Guanzheng, Wei Ziyao, Bao Zhiming, Yang Yang, Chen Xiaoping, Tao Wei, Li Yong, Zhang Yinghua, Tian Lixin, Gao Minliang, Li Yonggang, Liu Xiangyuan, Liu Wei, Lin Zhengping, Du Yingjun, Zhang Zemin, Su Wenjie, Yao Yuan, and Hu Bing.

introduction

At present, knowledge graphs have been widely used in finance, securities, biomedicine, manufacturing, transportation, education, agriculture, telecommunications, e-commerce, publishing and other industries. Rich application scenarios. As a specific carrier for the implementation of knowledge graphs in enterprises or institutions, the knowledge graph application platform supports enterprises and industries Intelligent accumulation, extraction, mining, circulation and management of knowledge, and providing knowledge services and system management functions required by enterprises. GB/T 45923 "Artificial Intelligence Knowledge Graph Application Platform" is planned to consist of four parts. --- Part 1.Functional Requirements. The purpose is to specify the system architecture and functional requirements of the knowledge graph application platform. --- Part 2.Performance requirements and test methods. The purpose is to specify the performance requirements and corresponding tests of the knowledge graph application platform method. --- Part 3.Knowledge Services. The purpose is to specify knowledge services such as knowledge question answering, knowledge retrieval and knowledge recommendation based on knowledge graphs. Service requirements. --- Part 4.Knowledge Management. The purpose is to specify the knowledge management requirements of the knowledge graph application platform. Artificial Intelligence Knowledge Graph Application Platform Part 2.Performance requirements and test methods 1 Scope This document specifies the performance requirements for the knowledge graph application platform and describes the corresponding testing methods. This document applies to the design, development, application, and performance testing of the knowledge graph application platform. 2 Normative references The contents of the following documents constitute the essential clauses of this document through normative references in this document. For referenced documents without a date, only the version corresponding to that date applies to this document; for referenced documents without a date, the latest version (including all amendments) applies to This document. GB/T 1988 Information technology - Seven-bit coded character set for information interchange GB/T 13000 Information Technology Universal Coded Character Set (UCS) GB 18030 Information Technology Chinese Coded Character Set 3 Terms and Definitions The following terms and definitions apply to this document. 3.1 knowledge Knowledge, judgment or skills acquired through learning, practice or exploration. [Source. GB/T 23703.2-2010, 2.1] 3.2 knowledge graph A collection of knowledge elements and their connections described in a structured form. [Source. GB/T 42131-2022, 3.6] 3.3 knowledge unit knowledge unit A collection of knowledge elements organized according to certain relationships. [Source. GB/T 42131-2022, 3.7] 3.4 A system that integrates knowledge graphs and business implementation logic, and has complete information flows and preset functions for knowledge graph construction and application. 3.5 The proportion of correct knowledge in the acquired knowledge. ...