博文
DIKWP updates Rule of Law from Conceptual to Semantic Space
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DIKWP 引领法治从概念空间到语义空 间的认知空间升维
November 2023
DOI:
Yucong Duan
Gong ShimingTraditional Invention and Innovation Theory 1946-TRIZ Does Not Adapt to the Digital Era
-Innovative problem-solving methods combining DIKWP model and classic TRIZ
Purpose driven Integration of data, information, knowledge, and wisdom Invention and creation methods: DIKWP-TRIZ
(Chinese people's own original invention and creation methods:DIKWP - TRIZ)
DIKWP updates the Rule of Law from Conceptual Space to Semantic Space in Cognitive Space
Prof. Yucong Duan
Benefactor: Shiming Gong
DIKWP-AC Artificial Consciousness Laboratory
AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory
(Email：duanyucong@hotmail.com)
Catalogue
2 The need to shift the rule of law from conceptual to semantic space in the digital age
2.1 The context of the digital age and its impact on legal practice
2.1.1 The revolution in information technology
2.1.2 New challenges to legal practice
2.2 The need to move from conceptual to semantic space
2.3 Application Case Study: WeChat Account Stealing and Selling
3. Limitations of traditional legal treatment and the application of the DIKWP model
3.1.1 Limitations of traditional legal treatment
3.1.2 DIKWP Semantic Modelling Application
3.2.1 Limitations of traditional legal treatment
3.2.2 DIKWP Semantic Modelling Application
4.1 Application of the DIKWP model
4.2 The role of DIKWP semantic transformations
4.3 Details of the DIKWP model in case handling
In the wave of the digital age, the legal field is facing unprecedented challenges and changes. The rapid development of information technology and the continued expansion of cyberspace have not only dramatically altered the structure of society and people's way of life, but have also placed new demands on the traditional rule of law model. In the context of this era, the effective implementation of the rule of law no longer relies solely on fixed legal concepts and provisions, but needs to shift to a more flexible and in-depth semantic space in order to adapt to the increasingly complex and changing legal environment.
The purpose of this paper is to explore in depth the necessity of the shift of the rule of law from the conceptual space to the semantic space in the digital era, and to analyse the motivation behind this shift and its impact on legal practice. We will focus on the emergence of new forms of crime, privacy and data protection in the context of digitisation and the challenges they pose to traditional legal frameworks. The article will also introduce legal solutions based on the DIKWP (Data, Information, Knowledge, Wisdom, and Purpose) model and demonstrate the application of this model and its advantages in real-life legal issues through practical cases, such as the theft and sale of WeChat and QQ accounts.
Through this in-depth discussion, we hope to reveal the importance and urgency of the transformation of the rule of law to a semantic space in the digital era, and to provide legal professionals with new perspectives and ideas when dealing with the legal challenges of the new era.
2 The need to shift the rule of law from conceptual to semantic space in the digital age
With the advent of the digital age, the legal field faces unprecedented challenges. The massive generation and flow of data, the widespread use of information technology, and the evolution of forms of crime require a fundamental shift in the legal system from the traditional concept-driven model to a semantics-based processing model. The present report will provide insights into the context, necessity and advantages of such a shift.
2.1 The context of the digital age and its impact on legal practice
The advent of the digital age not only marks the profound impact of information technology on society, but especially in the field of legal practice, it also heralds a fundamental challenge to traditional legal concepts and practices. This Part will explore in detail the context of the digital age and, in particular, how it is changing the face of legal practice.
2.1.1 The revolution in information technology
Over the past few decades, information technology has undergone tremendous changes. The spread of the Internet and the widespread use of smart devices have revolutionised the way people access, process and transmit information. This change has been further enhanced by the development of cloud computing and big data technologies, which have brought unprecedented efficiency and convenience, while at the same time triggering new social, economic and cultural dynamics.
Data explosion: A distinctive feature of the digital age is the explosion of data. Every day, huge amounts of data are generated and exchanged, from personal activities to global events, providing an unprecedented source of information for decision-making, while at the same time posing the challenge of data management and utilisation.
Expansion of cyberspace: The Internet has become an integral part of modern society, and cyberspace is gradually becoming an extension of real life. People socialise, conduct business transactions, study and entertain themselves on the Internet, which not only changes the way of interpersonal communication, but also puts new demands on the law.
Advances in smart technologies: the development of technologies such as artificial intelligence, machine learning and the Internet of Things (IoT) not only provide new tools to deal with complex issues, but also bring new challenges and risks. For example, the definition of legal liability for automated and intelligent decision-making processes has become increasingly complex.
2.1.2 New challenges to legal practice
With advances in technology, legal practice has encountered unprecedented challenges:
New forms of crime: The emergence of new forms of crime, such as cybercrime, data breaches and online fraud, poses a serious challenge to the traditional legal system. These crimes often cross national borders and involve complex technical issues, making the application and enforcement of the law difficult.
Privacy and data protection: With the massive collection and use of personal data, privacy and data protection have become hot topics. Finding a balance between promoting technological development and protecting personal privacy has become a key issue for the law to address.
Protection of intellectual property rights: The definition and protection of intellectual property rights in the digital age has become more complex. The rapid dissemination and use of digital content has brought challenges to copyright protection, and how to protect the legitimate rights and interests of creators while promoting the free flow of information has become a new issue for the law to face.
Difficulties in the application of the law: The speed of technological development far exceeds the speed of updating the law, resulting in the law often having difficulty in adapting to emerging technologies and phenomena in a timely manner. For example, legal provisions on the Internet often lag behind the development of Internet technology, making the law ineffective in its practical application.
2.2 The need to move from conceptual to semantic space
In the context of the digital age, the practice of law needs to shift from the traditional mode of processing that relies on fixed concepts to a more flexible semantic-based mode of processing. This shift means that the law no longer relies solely on strictly defined legal terms and concepts, but needs to understand and interpret the semantic content of the law according to the specific context of the case. This shift is critical to adapting to the legal challenges of the digital age, not only by improving the precision and consistency of legal judgements, but also by better responding to emerging legal issues. Through this shift, legal practice can more effectively respond to the complexity and diversity of the digital age and achieve fairer and more efficient legal decisions.
Necessity
Responding to complexity: The digital age has brought with it an explosion in the volume of data and an acceleration in the flow of information, as well as the resulting complexity of social, economic and cultural issues. The practice of law needs to be able to flexibly adapt to this complexity, and adherence to traditional concept-driven legal processing models is no longer sufficient to meet these challenges.
Improved precision and consistency: Semantic-based processing can improve the precision of legal judgements by more accurately capturing and reflecting the reality of a case. This is essential to ensure the fairness and consistency of the law.
Adapting to technological developments: Rapid technological developments continue to bring about new legal issues, such as cybercrime and data privacy protection. These issues often involve complex technological backgrounds that require the law to be able to adapt quickly and respond effectively.
Advantages
Flexibility and adaptability: The semantics-driven legal model allows for more flexible legal interpretation and application. It enables the interpretation and application of the law to be adapted to the specific context of a case, thus better meeting the needs of a changing society.
Comprehensiveness and depth: Semantic-based legal processing focuses not only on the legal text itself, but also on its meaning and application in a particular context. This approach allows for a more comprehensive consideration of all perspectives of a case and more in-depth analysis and judgement.
Improved legal efficiency: The semantic-driven model enables the practice of law to deal with complex issues more quickly, improving the efficiency of legal practice. This advantage is especially evident in cases involving large amounts of data and complex situations.
2.3 Application Case Study: WeChat Account Stealing and Selling
In the digital age, the increasing number of cases in which WeChat accounts are stolen and sold is not only a new form of crime, but also a serious challenge to the traditional legal framework. WeChat accounts are closely related to an individual's communication, property and even social relationships, and their security touches on a variety of areas such as digital identity, network security and personal privacy.
Limitations of the traditional legal framework
Under the traditional concept-driven legal model, such offences are difficult to define and deal with effectively due to their novelty and complexity. For example, traditional laws may treat such cases as property infringement only, ignoring the deeper issues involved, such as cybersecurity and personal privacy. In addition, traditional laws often lack specific provisions for digital offences, leaving a lack of legal basis for combating and preventing such offences.
Treatment under the semantics-driven model
Under a semantics-driven legal model, legal experts and law enforcement agencies can understand and deal with such cases in a more comprehensive and in-depth manner.
Value of digital identity: Under the semantics-driven model, legal experts are able to recognise the significant value of WeChat accounts as digital identities. The personal information, social network and property information contained in WeChat accounts make them important identifiers for individuals in the digital world.
Individual privacy protection: this type of offence violates the right to privacy of individuals, including the privacy of communications and the privacy of personal data. Semantic-based legal analysis can provide more precise legal protection by focusing on the protection of personal privacy.
Importance of network security: the security of WeChat accounts is directly related to network security and data security. Semantics-driven legal processing focuses not only on the judgement of individual cases, but also on how to improve the security of the whole cyberspace through legal regulation.
Legal regulation and punitive measures: In the semantics-driven model, the law is not only a punishment for criminal offences, but also a prevention of similar offences in the future. Lawmakers can formulate more effective legal regulation and punishment measures based on the characteristics of such crimes, such as increasing criminal liability for cybercrime and strengthening security measures for online accounts.
It can be seen that traditional laws face many limitations in dealing with new types of crimes in the digital era. In contrast, the semantic-based legal processing model can provide a more comprehensive and in-depth understanding of all perspectives of a case, leading to a more accurate and effective judgement. This not only helps to improve the accuracy and fairness of legal judgements, but also helps the law to better adapt to the challenges of the digital era, protect individual rights and maintain cyber security.
Summary
In the digital era, the shift from conceptual space to semantic space in the legal field is necessary and urgent. This shift can not only help the law better adapt to the challenges posed by emerging technologies, but also improve the precision, flexibility and efficiency of legal processing. Through this shift, legal practice can respond more effectively to the complexity and diversity of the digital age and achieve more just and efficient legal decisions.
3. Limitations of traditional legal treatment and the application of the DIKWP model
3.1 Limitations of Traditional Legal Processing and DIKWP Semantic Solution: The Case of Stolen and Sold QQ Accounts
In legal education, we are faced with the challenge of adapting traditional legal frameworks to the digital age. This report demonstrates the limitations of traditional legal treatment by analysing a specific case of QQ account theft and sale, and proposes a semantic solution based on the DIKWP model.
3.1.1 Limitations of traditional legal treatment
In this case, a QQ account was stolen by hackers and sold on the Internet. Traditional legal treatment tends to treat this as a simple property infringement, ignoring the multi-dimensional issues of digital identity, personal privacy and cybersecurity involved. In addition, cybercrime of a cross-border nature presents difficulties in the application and enforcement of the law.
3.1.2 DIKWP Semantic Modelling Application
Data layer (D)
Case data analysis: First, activity logs and transaction records of stolen QQ accounts are collected from reports obtained by the police. Analysing these data can reveal patterns of account theft and selling, such as changes in login location, transaction frequency and amount of money involved.
Semantic understanding of the data: frequent location changes in the login logs may imply that the account was illegally accessed. From the transaction logs, the point in time and frequency of accounts being sold can be inferred.
Information Layer (I)
Information extraction of the case: based on data analysis, key information is extracted, such as the specific time when the account was stolen, the way the crime was committed, and the loss suffered by the victim.
Semantic analysis of information: further analyse the victim's loss, such as the extent of personal information leakage, and the potential risks (e.g. property loss, privacy leakage) after the account is stolen.
Knowledge Layer (K)
Legal knowledge application: apply cybercrime laws, privacy laws and property rights laws to legally classify the case. For example, characterising account theft as a cybercrime and treating personal information leakage as a privacy violation.
Semantic application of knowledge: in-depth parsing of relevant legal provisions, how they apply to the scenario of cyber account theft, as well as its consequences and legal responsibilities.
Wisdom Layer (W)
Comprehensive judgement and decision-making: based on the legal knowledge, consider the impact of the case on the victim, cybersecurity and society, and formulate a reasonable judgement and preventive measures.
Semantic application of wisdom: considerations are not limited to punishing the offender, but also focus on preventing similar offences in the future through legal regulation and social education.
Purpose Layer (P)
Solution formulation: Define the objectives of case handling, such as strengthening cyber security measures and raising users' awareness of personal information protection.
Semantic realisation of purpose: design specific legal solutions based on case analysis, e.g. improving cybersecurity regulations and enhancing the fight against cybercrime.
Case processing details
Data layer details: Through detailed log analysis, it was found that the account frequently changed its IP address during the period of theft, a pattern significantly different from normal usage behaviour. In addition, records of account sales on the online black market indicate the commercial nature of the crime.
Information layer details: Information extracted from the data showed that the victims suffered significant property losses as well as leakage of personal information (e.g., address book, chat logs) after their accounts were stolen.
Knowledge layer details: By comparing historical cases similar to this type of crime, trends in the law's handling of such cases and the basis of judgement can be identified.
Wisdom layer detail: When evaluating cases, the overall impact of cybersecurity is taken into account, as well as how to reduce the occurrence of such crimes through legal and social means.
Purpose layer detail: after the goal was set, specific strategies were developed, including raising the security awareness of cyber users and strengthening the law's penalties for cybercrime.
Summary
Through in-depth analysis of the DIKWP model, the case of QQ account theft and sale was dealt with in a comprehensive and in-depth manner.The DIKWP model not only improves the accuracy and efficiency of the case processing, but also helps us to better understand and prevent this kind of cybercrime, so as to protect the individual's rights and safeguard the cybersecurity. This approach provides new perspectives and tools for legal practice in the digital age, and helps to develop the comprehensive analytical skills and critical thinking of law students.
3.2 Limitations of traditional legal treatment and the DIKWP semantic solution: the case of WeChat account theft and selling
In the digital era, traditional legal processing models face many limitations, especially when dealing with emerging issues involving cyber security and personal privacy. Taking the case of the theft and sale of WeChat accounts as an example, this analysis will show how these limitations can be overcome and such cases can be effectively resolved through the DIKWP semantic model.
3.2.1 Limitations of traditional legal treatment
Under the traditional legal framework, cases of WeChat accounts being stolen and sold are usually treated as simple property torts. This treatment ignores the complexity of the case, for example:
Importance of digital identity: traditional law fails to fully recognise the value and impact of WeChat accounts as digital identities.
Personal privacy and data security: lack of in-depth understanding of the impact of personal data breaches and their consequences.
Cross-border nature of cybercrime: failure to effectively address the cross-border aspects of cybercrime.
3.2.2 DIKWP Semantic Modelling Application
Data Layer (D)
Case data analysis: collect transaction records, chat logs, login logs, etc. of the WeChat accounts involved in the case and analyse the data to reveal patterns of account theft and selling.
Semantic understanding of data: Understand the meaning behind the data, e.g. login logs suggest unusual activities that may point to illegal access.
Information Layer (I)
Information extraction of the case: Extract key information from the data, such as when the account was stolen, how the crime was committed, and the extent of damage to the victim.
Semantic analysis of the information: analysing the information to understand the motivation and impact behind the criminal act, e.g. the actual consequences of a personal privacy breach.
Knowledge Layer (K)
Application of legal knowledge: classifying and interpreting cases legally in relation to cyber laws, privacy laws and property rights laws.
Semantic application of knowledge: using legal knowledge to understand and explain specific situations and consequences of cybercrime.
Wisdom Layer (W)
Integrated judgement and decision-making: integrating all perspectives of a case, including legal, ethical and social implications, such as the wider impact of cybersecurity on society.
Semantic application of wisdom: formulating a judgement based on the complexity of the case, considering not only the punishment of the offender but also the prevention of similar offences in the future.
Purpose Layer (P)
Solution formulation: specifying the goal of solving the case, e.g., enhancing cybersecurity, protecting personal privacy, etc.
Semantic realisation of purpose: designing specific legal solutions and policy recommendations to ensure that the solution achieves the stated goal.
Case Processing: highlighting the capabilities and advantages of DIKWP semantic transformation and verification
Under the DIKWP model, the case of WeChat accounts being stolen and sold is handled in a more comprehensive and in-depth manner:
Data layer and information layer: through in-depth analysis of the case data and information refinement, it is able to reveal the specific pattern of the crime and the actual loss of the victim, providing a solid foundation for case processing.
Knowledge and Wisdom Layer: Combining legal knowledge and a comprehensive assessment of the case, it not only explains the legal nature of the offence, but also considers the ethical and social implications of the case, and how to strengthen cybersecurity and protect personal privacy through legal regulation.
purpose Layer: Guided by clear objectives, specific solutions and policy recommendations were designed, such as strengthening security measures for online accounts and raising public awareness of cybersecurity.
Summary
With the DIKWP model, the handling of WeChat account theft and sale cases is not only more precise and effective, but also provides deeper insights and comprehensive solutions. This approach not only improves the accuracy and fairness of legal judgements, but also provides an effective way to prevent and combat such new cybercrimes. Through the DIKWP semantic model, legal practice can better adapt to the challenges of the digital age, protect individual rights and maintain cybersecurity.
4. Application of the DIKWP model to the field of legal jurisprudence: efficient processing and semantic calibration
The legal jurisprudence profession is facing unprecedented challenges and changes in the digital era.The DIKWP model (Data, Information, Knowledge, Wisdom, and Purpose), as an emerging processing framework, provides new perspectives for effective processing of legal issues and semantic calibration, especially in dealing with wrongful convictions that may result from errors in evidence collection. This presentation will provide insights into how the DIKWP model can provide more effective processing in the field of legal jurisprudence, especially in the perspective of statistics, reasoning and semantic calibration.
4.1 Application of the DIKWP model
Data layer (D)
Evidence collection and processing: Collect all data relevant to the case, including witness statements, physical evidence, paper records, etc. Data science methods are used to process and analyse this data to ensure the accuracy and completeness of the information.
Statistical analysis of data: Use statistical methods to analyse evidence data, identify correlations and patterns between data, and provide a quantitative basis for further analysis of the case.
Information Layer (I)
Information Refining and Interpretation: Transform data into information and refine key information points of the case by understanding the context and meaning behind the data.
Reasoning Analysis: logical reasoning based on information, constructing possibilities of case development and assumptions of different scenarios to help understand the real situation of the case.
Knowledge Layer (K)
Application of Legal Knowledge: Combining laws, regulations and precedents to provide legal interpretations of case information to ensure accuracy of understanding and application.
Comparative analysis of cases: Comparative analysis with historical cases to identify similarities and differences and provide legal knowledge to support case handling.
Wisdom Layer (W)
Comprehensive Assessment and Decision Making: Comprehensive assessment of the case, taking into account legal, ethical and social influences, to form a wise decision.
Prediction and Prevention: Based on in-depth analysis and comprehensive assessment, predict the possible development of the case and propose preventive measures and strategies to avoid future wrongful convictions.
Purpose Layer (P)
Goal Clarification and Realisation: Clarify the goals of case handling, such as finding out the truth, protecting the rights and interests of victims, and ensuring a fair trial.
Solution design: designing specific legal solutions based on the objectives, such as trial programmes, policy recommendations, etc.
4.2 The role of DIKWP semantic transformations
Compensation and Calibration
The DIKWP model can be processed more efficiently at all levels through transformational compensation of semantics:
Compensation for data deficiencies: when some evidence or data in a case is incomplete, the analysis through the information and knowledge layers can compensate for this deficiency and provide a more comprehensive understanding of the case.
Semantic calibration: In the face of possible errors in evidence collection, the DIKWP model is able to break through the limitations of a single conceptual level by calibrating at the semantic level, thus avoiding the occurrence of wrongful convictions.
Advantages
Improved accuracy: With the DIKWP model, legal practices are able to handle and understand cases more accurately, especially in complex or controversial cases.
Enhanced legal adaptability: The DIKWP model improves the adaptability of legal practice to emerging technologies and complex contexts, making the law more effective in meeting the challenges of the digital age.
Promoting Fair Trials: Through comprehensive and in-depth analyses, the DIKWP model helps promote a more fair and transparent trial process.
4.3 Details of the DIKWP model in case handling
Data layer (D)
Abnormal activity monitoring: Using data analysis tools, historical activity analysis of the victim's WeChat account is conducted to identify abnormal changes in activity before and after the account is stolen.
Transaction record analysis: Detailed analysis of account-related financial transaction records to track the flow of funds to determine the economic impact of the crime.
Information Layer (I)
Victim Impact Assessment: Assess the victim's loss in terms of personal privacy and social networking based on chat records and account usage.
Crime Pattern Identification: Reveal the behavioural characteristics and modus operandi of offenders by analysing the operational patterns of account theft and sales.
Knowledge Layer (K)
Legal Interpretation: Detailed analysis of legal provisions involved in the case, such as cybercrime legal provisions, providing legal interpretation and application.
Precedent Reference: Citing precedents of judgements in similar cases to provide legal basis and handling reference for the case.
Wisdom Layer (W)
Risk assessment and strategy: Consider the impact of the case on the trust of the cyber society and assess the possible long-term consequences of the handling strategy, such as the promotion of cyber security and the protection of privacy.
Comprehensive Decision Making Recommendations: Integrate the legal, ethical and social impacts, and propose punishments for the offenders and compensation options for the victims.
Purpose Layer (P)
Long-term goal planning: Setting long-term goals for preventing similar crimes in the future, such as strengthening security and protection measures for online accounts through legal reform.
Specific measure design: design specific legal measures, including judgement enforcement, victim compensation and prevention policy development.
Summary
The application of the DIKWP model in the field of legal jurisprudence demonstrates its power in dealing with complex legal problems, especially in the perspectives of statistics, reasoning and semantic calibration. Through this model, legal professionals are able to handle cases more efficiently and ensure the accuracy and fairness of legal judgements.The DIKWP model provides a new perspective and approach to legal practice, which helps to better adapt to and cope with the challenges of the digital age. It also provides a framework for legal professionals to deal with complex cybercrime cases in a comprehensive and in-depth manner. Through this approach, every perspective of a case can be understood more effectively, leading to more accurate and fairer judgements. Especially in cases where there may be errors in evidence collection, the DIKWP model can significantly reduce the risk of wrongful convictions through semantic transformation and calibration. This model not only improves the efficiency and accuracy of legal practice, but also provides new perspectives and methods for the future development of the legal profession.
This paper explores the need to shift the rule of law from conceptual to semantic space in the digital age and highlights the limitations of the traditional legal model in responding to the information technology revolution and the new forms of crime it has brought about. Features of the digital age, such as the explosion of data, the expansion of cyberspace and the advancement of smart technologies, pose unprecedented challenges to legal practice, including the emergence of new forms of crime, issues of privacy and data protection, the complexity of intellectual property rights protection, and difficulties in the application of the law.
By analysing cases such as the theft and sale of WeChat and QQ accounts, this paper demonstrates how a semantic-based legal processing model can provide a more comprehensive and in-depth solution. This model is capable of understanding and interpreting the semantic content of the law according to the specific context of the case, better adapting to the challenges of the digital age, and improving the precision and consistency of legal judgements.
Going forward, legal practice needs to shift from the traditional concept-driven model to a flexible semantic-based processing model. Such a shift will help the law to better cope with the complexity and diversity of the digital age and achieve more just and efficient legal judgements, while playing an important role in the perspective of protecting the rights of individuals and maintaining cybersecurity.
Yucong Duan, male, currently serves as a member of the Academic Committee of the School of Computer Science and Technology at Hainan University. He is a professor and doctoral supervisor and is one of the first batch of talents selected into the South China Sea Masters Program of Hainan Province and the leading talents in Hainan Province. He graduated from the Software Research Institute of the Chinese Academy of Sciences in 2006, and has successively worked and visited Tsinghua University, Capital Medical University, POSCO University of Technology in South Korea, National Academy of Sciences of France, Charles University in Prague, Czech Republic, Milan Bicka University in Italy, Missouri State University in the United States, etc. He is currently a member of the Academic Committee of the School of Computer Science and Technology at Hainan University and he is the leader of the DIKWP (Data, Information, Knowledge, Wisdom, Purpose) Innovation Team at Hainan University, Distinguished Researcher at Chongqing Police College, Leader of Hainan Provincial Committee's "Double Hundred Talent" Team, Vice President of Hainan Invention Association, Vice President of Hainan Intellectual Property Association, Vice President of Hainan Low Carbon Economy Development Promotion Association, Vice President of Hainan Agricultural Products Processing Enterprises Association, Visiting Fellow, Central Michigan University, Member of the Doctoral Steering Committee of the University of Modena. Since being introduced to Hainan University as a D-class talent in 2012, He has published over 260 papers, included more than 120 SCI citations, and 11 ESI citations, with a citation count of over 4300. He has designed 241 serialized Chinese national and international invention patents (including 15 PCT invention patents) for multiple industries and fields and has been granted 85 Chinese national and international invention patents as the first inventor. Received the third prize for Wu Wenjun's artificial intelligence technology invention in 2020; In 2021, as the Chairman of the Program Committee, independently initiated the first International Conference on Data, Information, Knowledge and Wisdom - IEEE DIKW 2021; Served as the Chairman of the IEEE DIKW 2022 Conference Steering Committee in 2022; Served as the Chairman of the IEEE DIKW 2023 Conference in 2023. He was named the most beautiful technology worker in Hainan Province in 2022 (and was promoted nationwide); In 2022 and 2023, he was consecutively selected for the "Lifetime Scientific Influence Ranking" of the top 2% of global scientists released by Stanford University in the United States. Participated in the development of 2 international standards for IEEE financial knowledge graph and 4 industry knowledge graph standards. Initiated and co hosted the first International Congress on Artificial Consciousness (AC2023) in 2023.
Data can be regarded as a concrete manifestation of the same semantics in our cognition. Often, Data represents the semantic confirmation of the existence of a specific fact or observation, and is recognised as the same object or concept by corresponding to some of the same semantic correspondences contained in the existential nature of the cognitive subject's pre-existing cognitive objects. When dealing with data, we often seek and extract the particular identical semantics that labels that data, and then unify them as an identical concept based on the corresponding identical semantics. For example, when we see a flock of sheep, although each sheep may be slightly different in terms of size, colour, gender, etc., we will classify them into the concept of "sheep" because they share our semantic understanding of the concept of "sheep". The same semantics can be specific, for example, when identifying an arm, we can confirm that a silicone arm is an arm based on the same semantics as a human arm, such as the same number of fingers, the same colour, the same arm shape, etc., or we can determine that the silicone arm is not an arm because it doesn't have the same semantics as a real arm, which is defined by the definition of "can be rotated". It is also possible to determine that the silicone arm is not an arm because it does not have the same semantics as a real arm, such as "rotatable".
Information, on the other hand, corresponds to the expression of different semantics in cognition. Typically, Information refers to the creation of new semantic associations by linking cognitive DIKWP objects with data, information, knowledge, wisdom, or purposes already cognised by the cognising subject through a specific purpose. When processing information, we identify the differences in the DIKWP objects they are cognised with, corresponding to different semantics, and classify the information according to the input data, information, knowledge, wisdom or purpose. For example, in a car park, although all cars can be classified under the notion of 'car', each car's parking location, time of parking, wear and tear, owner, functionality, payment history and experience all represent different semantics in the information. The different semantics of the information are often present in the cognition of the cognitive subject and are often not explicitly expressed. For example, a depressed person may use the term "depressed" to express the decline of his current mood relative to his previous mood, but this "depressed" is not the same as the corresponding information because its contrasting state is not the same as the corresponding information. However, the corresponding information cannot be objectively perceived by the listener because the contrasting state is not known to the listener, and thus becomes the patient's own subjective cognitive information.
Knowledge corresponds to the complete semantics in cognition. Knowledge is the understanding and explanation of the world acquired through observation and learning. In processing knowledge, we abstract at least one concept or schema that corresponds to a complete semantics through observation and learning. For example, we learn that all swans are white through observation, which is a complete knowledge of the concept "all swans are white" that we have gathered through a large amount of information.
Wisdom corresponds to information in the perspective of ethics, social morality, human nature, etc., a kind of extreme values from the culture, human social groups relative to the current era fixed or individual cognitive values. When dealing with Wisdom, we integrate this data, information, knowledge, and wisdom and use them to guide decision-making. For example, when faced with a decision-making problem, we integrate various perspectives such as ethics, morality, and feasibility, not just technology or efficiency.
Purpose can be viewed as a dichotomy (input, output), where both input and output are elements of data, information, knowledge, wisdom, or purpose. Purpose represents our understanding of a phenomenon or problem (input) and the goal we wish to achieve by processing and solving that phenomenon or problem (output). When processing purposes, the AI system processes the inputs according to its predefined goals (outputs), and gradually brings the outputs closer to the predefined goals by learning and adapting.
[1] Yucong Duan. Promoting the Spirit of Returned Overseas Chinese Serving the Nation, Exploring New Modes of Practising and Educating People. DOI: 10.13140/RG.2.2.30559.84642. https://www.researchgate.net/publication/375915934_hong_yang_gui_qiao_bao_guo_jing_shen_tan_suo_shi_jian_yu_ren_xin_mo_shi_hainandaxueqiaolianweiyuan_jisuanjikexuejishuxueyuan_--duanyucong_2023nian6yue28ri. 2023.
[2] Yucong Duan. Purpose driven big model semantic security for the convergence of data, information, knowledge, and wisdom: the DIKWP-LAW. DOI: 10.13140/RG.2.2.25946.11206. https://www.researchgate.net/publication/375915747_shujuxinxizhishizhihuironghedeyituqudongdamoxingyuyianquanDIKWP-LAW. 2023.
[3] Yucong Duan, Shiming Gong.Towards an Objective and Comprehensive IQ and EQ Measurement Standard - Based on DIKWP Semantic Space Orientation Construction. DOI: 10.13140/RG.2.2.24268.39048. https://www.researchgate.net/publication/375915512_Towards_an_Objective_and_Comprehensive_IQ_and_EQ_Measurement_Standard_-_Based_on_DIKWP_Semantic_Space_Orientation_Construction. 2023.
[4] Yucong Duan, Fuliang Tang, Zeyu Yang. Gender Bias Analysis of GPT4 in Emotional Quotient Evaluation --DIKWP Research Group. DOI: 10.13140/RG.2.2.25769.16488. https://www.researchgate.net/publication/375895127_Gender_Bias_Analysis_of_GPT4_in_Emotional_Quotient_Evaluation_--DIKWP_Research_Group. 2023.
[5] Yucong Duan. Exploration of the Application of the DIKWP Model in Judicial Decision Making. DOI: 10.13140/RG.2.2.17380.55683. https://www.researchgate.net/publication/375894692_Exploration_of_the_Application_of_the_DIKWP_Model_in_Judicial_Decision_Making. 2023.
[6] Yucong Duan, Fuliang Tang, Zhendong Guo, Zhendong Guo, Yingtian Mei, Yuxing Wang, Kunguang Wu, Zeyu Yang. Global AGI Large Language Model IQ and EQ Evaluation and Ranking --by DIKWP Research Group. DOI: 10.13140/RG.2.2.30894.08004. https://www.researchgate.net/publication/375837234_Global_AGI_Large_Language_Model_IQ_and_EQ_Evaluation_and_Ranking_--by_DIKWP_Research_Group. 2023.
[7] Yucong Duan, Zeyu Yang, Yingtian Mei. How high is Mr GPT4's Intelligence Quotient? --DIKWP Group International Standard Evaluation. DOI: 10.13140/RG.2.2.24206.95044. https://www.researchgate.net/publication/375775422_GPT4_xianshengdezhishangyouduogao_--DIKWP_tuanduiguojibiaozhunceping_DIKWP-TRIZ_DIKWP-AC_Artificial_Consciousness_Laboratory_AGI-AIGC-GPT_Evaluation_DIKWP_Global_Laboratory_Emailduanyuconghotmailcom. 2023
[8] Yucong Duan, Zeyu Yang. How high is Mr GPT4's Emotional Quotient? --DIKWP Group International Standard Evaluation. DOI: 10.13140/RG.2.2.18020.35205. https://www.researchgate.net/publication/375770961_GPT4_xianshengdeqingshangyouduogao_--DIKWP_tuanduiguojibiaozhunceping_DIKWP-TRIZ_DIKWP-AC_Artificial_Consciousness_Laboratory_AGI-AIGC-GPT_Evaluation_DIKWP_Global_Laboratory_Emailduanyuconghotmailcom. 2023.
[9] Yucong Duan, Yuxing Wang. How high is Claude-instant's Intelligence Quotient? --DIKWP Group International Standard Evaluation. DOI: 10.13140/RG.2.2.25884.67204. https://www.researchgate.net/publication/375776922_Claude-instant_zhishangIQyouduo_gao_--DIKWP_tuanduiguojibiaozhunceping_DIKWP-TRIZ_DIKWP-AC_Artificial_Consciousness_Laboratory_AGI-AIGC-GPT_Evaluation_DIKWP_Global_Laboratory_Emailduanyuconghotmailcom. 2023.
[10] Yucong Duan, Yuxing Wang. How high is Mr GPT4's Intelligence Quotient? --DIKWP Group International Standard Evaluation. DOI: 10.13140/RG.2.2.35321.85603. https://www.researchgate.net/publication/375773807_Claude-instant_damoxing_qingshangyouduogao_--DIKWP_tuanduiguojibiaozhunceping_DIKWP-TRIZ_DIKWP-AC_Artificial_Consciousness_Laboratory_AGI-AIGC-GPT_Evaluation_DIKWP_Global_Laboratory_Emailduanyuconghot. 2023.
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