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A Practical Approach to Constructing a Knowledge Graph for Cybersecurity Article
Yan Jia, Yulu Qi, Huaijun Shang, Rong Jiang, Aiping Li
Engineering 2018, Volume 4, Issue 1, Pages 53-60 doi: 10.1016/j.eng.2018.01.004
Cyberattack forms are complex and varied, and the detection and prediction of dynamic types of attack are always challenging tasks. Research on knowledge graphs is becoming increasingly mature in many fields. At present, it is very significant that certain scholars have combined the concept of the knowledge graph with cybersecurity in order to construct a cybersecurity knowledge base. This paper presents a cybersecurity knowledge base and deduction rules based on a quintuple model. Using machine learning, we extract entities and build ontology to obtain a cybersecurity knowledge base. New rules are then deduced by calculating formulas and using the path-ranking algorithm. The Stanford named entity recognizer (NER) is also used to train an extractor to extract useful information. Experimental results show that the Stanford NER provides many features and the useGazettes parameter may be used to train a recognizer in the cybersecurity domain in preparation for future work.
Keywords： Cybersecurity Knowledge graph Knowledge deduction
An error recognition method for power equipment defect records based on knowledge graph technology Regular Article
Hui-fang Wang, Zi-quan Liu,huifangwang@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 11, Pages 1564-1577 doi: 10.1631/FITEE.1800260
Keywords： 错误识别；电力设备缺陷记录；知识图谱；机器学习
Technologies and Applications of Big Data Knowledge Engineering for Smart Taxation Systems
Zheng Qinghua , Shi Bin , Dong Bo
Strategic Study of CAE 2023, Volume 25, Issue 2, Pages 221-231 doi: 10.15302/J-SSCAE-2023.07.005
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.
Keywords： smart taxation knowledge engineering big data knowledge graph knowledge reasoning
AI Assisted Clinical Diagnosis & Treatment, and Development Strategy
Kong Ming,He Qianfeng and Li Lanjuan
Strategic Study of CAE 2018, Volume 20, Issue 2, Pages 86-91 doi: 10.15302/J-SSCAE-2018.02.013
The integration, open accessing of healthcare data, and the use of artificial intelligence to organize and analyze fragmented medical information can improve medical and health services, promote the level of rational government decision-making, and reduce the inequality in the allocation of medical and health resources. This paper summarizes the current status of technologies and applications of artificial intelligence in the field of medical information semantic fusion and in the field of image analysis, and analyzes current problems and challenges. The first is the standardized representation and structural integration of medical information to merge national and widely-used clinical terminologies, which is key to realizing auxiliary diagnosis based on ‘big data’ artificial intelligent. The second is the use of massive medical knowledge to construct an intelligent diagnosis and treatment model with the ability to combine multimodal data analysis and structured knowledge reasoning. Thus, we propose a national-level healthcare open data cloud platform that can help open up new data markets, improve the integration of healthcare data, and provide the new service of knowledge discovery and services. We also suggest to establish some basic industry standards for medical and health information, to strengthen the research and development of domestic medical devices, to promote the development of intelligent medical devices and smart wearable devices, and to guide the industry to open up new markets on the combination of artificial intelligence and medical devices.
Keywords： artificial intelligence assisted diagnosis and treatment knowledge graph medical ontology medical image analysis
A knowledge-guided and traditional Chinese medicine informed approach for herb recommendation Research Article
Zhe JIN, Yin ZHANG, Jiaxu MIAO, Yi YANG, Yueting ZHUANG, Yunhe PAN,11521043@zju.edu.cn,yinzh@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 10, Pages 1416-1429 doi: 10.1631/FITEE.2200662
Keywords： Traditional Chinese medicine Herb recommendation Knowledge graph Graph attention network
Xinyu Li, Pai Zheng, Jinsong Bao, Liang Gao, Xun Xu
Engineering 2023, Volume 22, Issue 3, Pages 14-19 doi: 10.1016/j.eng.2021.08.018
A network security entity recognition method based on feature template and CNN-BiLSTM-CRF Research Papers
Ya QIN, Guo-wei SHEN, Wen-bo ZHAO, Yan-ping CHEN, Miao YU, Xin JIN
Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6, Pages 872-884 doi: 10.1631/FITEE.1800520
By network security threat intelligence analysis based on a security knowledge graph (SKG), multi-source threat intelligence data can be analyzed in a fine-grained manner. This has received extensive attention. It is difficult for traditional named entity recognition methods to identify mixed security entities in Chinese and English in the field of network security, and there are difficulties in accurately identifying network security entities because of insufficient features extracted. In this paper, we propose a novel FT-CNN-BiLSTM-CRF security entity recognition method based on a neural network CNN-BiLSTM-CRF model combined with a feature template (FT). The feature template is used to extract local context features, and a neural network model is used to automatically extract character features and text global features. Experimental results showed that our method can achieve an F-score of 86% on a large-scale network security dataset and outperforms other methods.
Keywords： Network security entity Security knowledge graph (SKG) Entity recognition Feature template Neural network
A Framework of Knowledge Theory： Toward a Unified Theory of Information, Knowledge and Intelligence
Zhong Yixin
Strategic Study of CAE 2000, Volume 2, Issue 9, Pages 50-64
Knowledge has been very important wealth to the mankind but there has not a knowledge theory existed yet till the present time. An attempt is thus made in the paper to present a framework of knowledge theory that includes two parts: fundamentals and the main body of knowledge theory. The first part is to deal with a series of basic issues such as the related concepts and definitions, the methods of representation, the measurements, the reasoning and decision rules. The second part is to explore the mechanism of knowledge formation based on information processing and the mechanism of intelligence formation based on the activation of knowledge. It is believed that the establishment of the knowledge theory will lay a solid foundation to the unified theory of information, knowledge, and intelligence and will greatly facilitate the effective utilization of information and knowledge, leading to the growth of the research in the field of intelligent machines.
Keywords： knowledge amount of knowledge knowledge formation knowledge activation unified theory of information-knowledge-intelligence
Miniaturized five fundamental issues about visual knowledge Perspectives
Yun-he Pan,panyh@zju.edu.cn
Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.2040000
Keywords： 视觉知识表达；视觉识别；视觉形象思维模拟；视觉知识学习；多重知识表达
The application and deepening of knowledge management system
Li Hua
Strategic Study of CAE 2011, Volume 13, Issue 8, Pages 87-93
In the knowledge economy era, enterprises begin to realize that the most valuable property is knowledge. Knowledge is not only the key element of production, but also an important means for enterprises to maintain a sustainable competitive advantage. Effectively implementing the knowledge management system should be guided by the correct principles. It is necessary to establish proper organizational structure and business culture for knowledge sharing; meanwhile, during the understanding and implementation of knowledge management, 5 principles of accumulation, trust, sharing, communication and learning must be adhered to. The goal of knowledge management for the organization is to achieve the overall development strategy, hence the knowledge sharing process must make the realization of organizational strategic objective as the prerequisite. The selection of sharing knowledge, building knowledge sharing platform and storage management unit, leading knowledge exchange and implementing incentive policies should be emphasized.
Keywords： knowledge management knowledge sharing knowledge forum
Development and Prospect of Big Data Knowledge Engineering
Zheng Qinghua, Liu Huan, Gong Tieliang, Zhang Lingling, Liu Jun
Strategic Study of CAE 2023, Volume 25, Issue 2, Pages 208-220 doi: 10.15302/J-SSCAE-2023.02.018
Big Data Knowledge Engineering is the infrastructure of artificial intelligence, a common requirement faced by various industries and fields, and the inevitable path for the digitalization to intelligence. In this paper, we firstly elaborate on the background and connotation of big data knowledge engineering and propose a research framework of “data knowledgeization, knowledge systematization, and knowledge reasoning”. Secondly, we sort out the key technologies of knowledge acquisition and fusion, knowledge representation, and knowledge reasoning and introduce engineering applications in typical scenarios such as smart education, tax risk control, and smart healthcare. Thirdly, we summary the challenges faced by big data knowledge engineering and predict the future research directions including complex big data knowledge acquisition, knowledge+data hybrid learning, and brain-inspired knowledge coding and memorizing. Finally, several suggestions are given by the research: guiding interdisciplinary integration and establishing major and key R&D projects to promote the basic theory and technological breakthroughs of big data knowledge engineering; strengthening communication and cooperation between enterprises and research institutions as well as promoting cutting-edge research results to form application demonstrations, so as to establish an industry-standard system for big data knowledge engineering; exploring school-enterprise cooperation in line with market demands, orienting towards major application needs, and accelerating the landing application of big data knowledge engineering technology in the country's important industries.
Keywords： Big Data Knowledge Engineering Knowledge Acquisition Knowledge Fusion Knowledge Representation Knowledge Reasoning
Establishing a New Discipline ： Knowledge Systems Engineering
Wang Zhongtuo
Strategic Study of CAE 2006, Volume 8, Issue 12, Pages 1-9
In this paper, the establishment of a new discipline, knowledge systems engineering, and the mission and contents of this new discipline are described. The architectures of the knowledge systems are suggested and the working processes are analyzed. Some new concepts about the creation of new knowledge are put forward.
Keywords： knowledge management systems engineering knowledge systems engineering innovation knowledge integration
Xie Youbai
Strategic Study of CAE 2013, Volume 15, Issue 4, Pages 14-22
The behaviors of knowledge in designing human purposeful activities are studied. The knowledge flow and the efforts in making the knowledge more complete via competition, i.e. the knowledge evolution in design are analyzed. The relations between innovation and design and the knowledge essentiality of innovation and design are discussed. The study reveals that design is based on existed knowledge and centered on new knowledge acquirement. The study also gives the incompleteness and competitiveness of knowledge in design. Since innovation has been identified as a basic driving force of the mode transformation of economic development, the behaviors of knowledge should be important issues to be studied in design science.
Keywords： design design science knowledge knowledge acquirement knowledge flow
Yang Bingru,Shen Jiangtao,Chen Hongjie
Strategic Study of CAE 2003, Volume 5, Issue 6, Pages 49-54
Knowledge discovery in knowledge base (KDK) is a brand-new task. Its success will directly act on the construction of large knowledge base, and, at present, it is important to the solving of the bottleneck of machine study—discovering knowledge. The main work of this paper is： The inductive structure of KDK based on the facts in knowledge base, and its algorithm and experimental verification; The inductive structure algorithm of KDK for the rules in knowledge base and its experimental verification.
Keywords： knowledge discovery based on knowledge base induction logic of Carnap induction logic of L. J. Cohen evaluation of hypothesis
Uncertainty in Knowledge Representation
Li Deyi
Strategic Study of CAE 2000, Volume 2, Issue 10, Pages 73-79
Knowledge representation in AI has been a bottleneck for years. And the difficulty is uncertainty hidden in qualitative concepts, that is the randomness and fuzziness. At this junction, this paper presents a new concept of cloud models with three digital characteristics： expected value Ex, entropy En, and hyper entropy He. This methodology has effectively made mapping between quantitative and qualitative knowledge much easier at any time. A cloud drop, that is a quantitative value, representing the qualitative concept can be measured by contributions. A new explanation for the 24 solar terms in lunar calendar is given as well. The cloud models have been used in data mining, intelligent control, hopping frequency technique, system evaluation, and so on.
Keywords： knowledge representation qualitative concept uncertainty cloud model digital characteristics
Title Author Date Type Operation
A Practical Approach to Constructing a Knowledge Graph for Cybersecurity
Yan Jia, Yulu Qi, Huaijun Shang, Rong Jiang, Aiping Li
Journal Article
An error recognition method for power equipment defect records based on knowledge graph technology
Hui-fang Wang, Zi-quan Liu,huifangwang@zju.edu.cn
Journal Article
Technologies and Applications of Big Data Knowledge Engineering for Smart Taxation Systems
Zheng Qinghua , Shi Bin , Dong Bo
Journal Article
AI Assisted Clinical Diagnosis & Treatment, and Development Strategy
Kong Ming,He Qianfeng and Li Lanjuan
Journal Article
A knowledge-guided and traditional Chinese medicine informed approach for herb recommendation
Zhe JIN, Yin ZHANG, Jiaxu MIAO, Yi YANG, Yueting ZHUANG, Yunhe PAN,11521043@zju.edu.cn,yinzh@zju.edu.cn
Journal Article
Achieving Cognitive Mass Personalization via the Self-X Cognitive Manufacturing Network: An Industrial Knowledge Graph- and Graph Embedding-Enabled Pathway
Xinyu Li, Pai Zheng, Jinsong Bao, Liang Gao, Xun Xu
Journal Article
A network security entity recognition method based on feature template and CNN-BiLSTM-CRF
Ya QIN, Guo-wei SHEN, Wen-bo ZHAO, Yan-ping CHEN, Miao YU, Xin JIN
Journal Article
A Framework of Knowledge Theory： Toward a Unified Theory of Information, Knowledge and Intelligence
Zhong Yixin
Journal Article
Miniaturized five fundamental issues about visual knowledge
Yun-he Pan,panyh@zju.edu.cn
Journal Article
Development and Prospect of Big Data Knowledge Engineering
Zheng Qinghua, Liu Huan, Gong Tieliang, Zhang Lingling, Liu Jun
Journal Article
Study into the knowledge in design science — Some important issues should be considered in mode transformation of economic development
Xie Youbai
Journal Article
Research on the Structure Model and Mining Algorithm for Knowledge Discovery Based on Knowledge Base (KDK)
Yang Bingru,Shen Jiangtao,Chen Hongjie
Journal Article
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