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A survey of script learning Review

Yi Han, Linbo Qiao, Jianming Zheng, Hefeng Wu, Dongsheng Li, Xiangke Liao,hanyi12@nudt.edu.cn,qiao.linbo@nudt.edu.cn,zhengjianming12@nudt.edu.cn,wuhefeng@mail.sysu.edu.cn,dsli@nudt.edu.cn,xkliao@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 3, Pages 287-436 doi: 10.1631/FITEE.2000347

Abstract: Script is the structured knowledge representation of prototypical real-life event sequences. Learning the commonsense knowledge inside the script can be helpful for machines in understanding natural language and drawing commonsensible inferences. is an interesting and promising research direction, in which a trained system can process narrative texts to capture script knowledge and draw inferences. However, there are currently no survey articles on , so we are providing this comprehensive survey to deeply investigate the standard framework and the major research topics on . This research field contains three main topics: event representations, models, and evaluation approaches. For each topic, we systematically summarize and categorize the existing systems, and carefully analyze and compare the advantages and disadvantages of the representative systems. We also discuss the current state of the research and possible future directions.

Keywords：脚本学习；自然语言处理；常识知识建模；事件推理

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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

Abstract:

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

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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

Abstract: 认知心理学早已指出，人类知识记忆中的重要部分是视觉知识，被用来进行形象思维。因此，基于视觉的人工智能（AI）是AI绕不开的课题，且具有重要意义。本文继《论视觉知识》一文，讨论与之相关的5个基本问题：（1）视觉知识表达；（2）视觉识别；（3）视觉形象思维模拟；（4）视觉知识的学习；（5）多重知识表达。视觉知识的独特优点是具有形象的综合生成能力，时空演化能力和形象显示能力。这些正是字符知识和深度神经网络所缺乏的。AI与计算机辅助设计/图形学/视觉的技术联合将在创造、预测和人机融合等方面对AI新发展提供重要的基础动力。视觉知识和多重知识表达的研究是发展新的视觉智能的关键，也是促进AI 2.0取得重要突破的关键理论与技术。这是一块荒芜、寒湿而肥沃的“北大荒”，也是一块充满希望值得多学科合作勇探的“无人区”。

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

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Visual commonsense reasoning with directional visual connections Research Articles

Yahong Han, Aming Wu, Linchao Zhu, Yi Yang,yahong@tju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.2000722

Abstract: To boost research into cognition-level visual understanding, i.e., making an accurate inference based on a thorough understanding of visual details, (VCR) has been proposed. Compared with traditional visual question answering which requires models to select correct answers, VCR requires models to select not only the correct answers, but also the correct rationales. Recent research into human cognition has indicated that brain function or cognition can be considered as a global and dynamic integration of local neuron connectivity, which is helpful in solving specific cognition tasks. Inspired by this idea, we propose a to achieve VCR by dynamically reorganizing the that is contextualized using the meaning of questions and answers and leveraging the directional information to enhance the reasoning ability. Specifically, we first develop a GraphVLAD module to capture to fully model visual content correlations. Then, a contextualization process is proposed to fuse sentence representations with visual neuron representations. Finally, based on the output of , we propose to infer answers and rationales, which includes a ReasonVLAD module. Experimental results on the VCR dataset and visualization analysis demonstrate the effectiveness of our method.

Keywords：视觉常识推理；有向连接网络；视觉神经元连接；情景化连接；有向连接

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The application and deepening of knowledge management system

Li Hua

Strategic Study of CAE 2011, Volume 13, Issue 8, Pages 87-93

Abstract:

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

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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

Abstract:

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

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Establishing a New Discipline ： Knowledge Systems Engineering

Wang Zhongtuo

Strategic Study of CAE 2006, Volume 8, Issue 12, Pages 1-9

Abstract:

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

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Study into the knowledge in design science — Some important issues should be considered in mode transformation of economic development

Xie Youbai

Strategic Study of CAE 2013, Volume 15, Issue 4, Pages 14-22

Abstract:

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

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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

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.

Keywords： smart taxation knowledge engineering big data knowledge graph knowledge reasoning

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Research on the Structure Model and Mining Algorithm for Knowledge Discovery Based on Knowledge Base (KDK)

Yang Bingru,Shen Jiangtao,Chen Hongjie

Strategic Study of CAE 2003, Volume 5, Issue 6, Pages 49-54

Abstract:

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

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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

Abstract:

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

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Uncertainty in Knowledge Representation

Li Deyi

Strategic Study of CAE 2000, Volume 2, Issue 10, Pages 73-79

Abstract:

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

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The Intellectual Property Rights Strategy of Enterprise and It´s Core Competency

Yuan Jun

Strategic Study of CAE 2004, Volume 6, Issue 11, Pages 88-92

Abstract:

At present, more and more enterprises are confronted with the problem of how to protect their intellectual property. So, it's very urgent for many enterprises to strengthen the management of their intellectual property. Hence, to design the strategy of protecting intellectual property, build the operation model based on it and reinforce the core competency is a matter of having profound senses. This paper puts forward the corresponding countermeasures and suggestions on the problem of protection of the intellectual property.

Keywords： intellectual property technology innovation core competency

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The Model on Maximum Entropy of Knowledge Creating Stochastic Process

Chen Xin,He Jinsheng,Dong Liping

Strategic Study of CAE 2004, Volume 6, Issue 12, Pages 43-46

Abstract:

On the basis of the hypothesis that the existing relevant knowledge influences the formation of the new knowledge, this thesis sets up the maximum entropy fundamental model of the stochastic prosess of knowledge innovation by applying the probability & statistics theory, the optimization theory and the maximum entropy principle comprehensively, and puts forward the conditional probability P(y|x) of the new knowledge y∈Y which is restrained by the existing knowledge x∈X. This model posesses such character that the randomness and the causality are unity of opposites

Keywords： maximum entropy knowledge innovation conditional entropy stochastic process

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The Research of Mining the Mutative Knowledge With Extension Data Mining

Chen Wenwei

Strategic Study of CAE 2006, Volume 8, Issue 11, Pages 70-73

Abstract:

The thesis standardized the extension information and the extension knowledge, that is, basing on the information and knowledge, the thesis extended the mutative information and knowledge, and nailed down the conceptions of extension data mining and extension reasoning. This thesis also attested two theorems of the extension data mining and the formula of the extension reasoning. The author put forward the extension data mining which extended from mining the static knowledge to mining the mutative knowledge. So it exploited a new aspect of data mining, and illuminated it by examples.

Keywords： extension information extension knowledge extension data mining extension reasoning