DIKWP Processing Report of the Big Five Personality Traits

• November 2023

• DOI: 

• 10.13140/RG.2.2.23767.85927

• Yucong Duan

DIKWP Processing Report of the Big Five Personality Traits

Yucong Duan

DIKWP-AC Artificial Consciousness Laboratory

AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory

DIKWP research group, Hainan University

duanyucong@hotmail.com

In attempting to mathematically interpret the Big Five personality traits in psychology using the DIKWP model, we can consider these personality traits as outputs in the human information processing system. The challenge here is to link these abstract psychological traits with a more concrete series of concepts: data, information, knowledge, wisdom, and purpose. Here is an attempt:

Openness:

Data: Observations of an individual's behaviors, choices, preferences, and reactions.

Information: Analysis of behavioral patterns revealing an individual's openness to new experiences and imagination.

Knowledge: Understanding the reasons and motivations behind an individual's behaviors and identifying their relationship with the trait of openness.

Wisdom: Making decisions that enhance individual quality of life and social welfare, using a deep understanding of openness.

Purpose: Fostering and utilizing openness to promote creativity and personal growth.

Conscientiousness:

Data: Specific details of an individual's performance in completing tasks and daily activities.

Information: Assessment of an individual’s reliability, organization, and efficiency.

Knowledge: Awareness of strategies to enhance a sense of responsibility and self-efficacy.

Wisdom: Applying conscientiousness in life decisions while considering long-term impacts.

Purpose: Pursuing an efficient and orderly lifestyle to achieve personal and professional goals.

Extraversion:

Data: Frequency of social activities and individual behaviors in social interactions.

Information: Assessing an individual’s social skills and their influence in groups.

Knowledge: Understanding how individuals build interpersonal relationships and social status through social behaviors.

Wisdom: Using interpersonal relationships and social activities to enhance personal and others' well-being.

Purpose: Actively participating in social networks to enhance self and others’ interests.

Agreeableness:

Data: Records of an individual’s behavior in cooperation and conflict resolution.

Information: Analysis of an individual’s level of empathy and cooperation in interpersonal interactions.

Knowledge: Deep understanding of behavioral patterns that promote harmonious social interactions.

Wisdom: Using the trait of agreeableness to promote personal relationships and community harmony.

Purpose: Establishing positive interpersonal relationships to achieve a more harmonious social environment.

Neuroticism:

Data: An individual’s emotional responses and stress levels in different situations.

Information: Analysis of an individual’s emotional response patterns, such as the frequency and intensity of emotional fluctuations.

Knowledge: Understanding the internal and external factors that affect emotional stability.

Wisdom: Developing and applying strategies to manage and adjust emotional responses.

Purpose: Achieving emotional stability to improve quality of life and mental health.

In a mathematical framework, these psychological traits can be modeled through a series of functions that transform observed behavior (data) into meaningful trait manifestations (information), then through deep understanding to form knowledge about individual personalities, and ultimately use this knowledge to guide decisions (wisdom) and achieve goals (purpose). This framework can help us understand and predict individual behaviors in quantitative research.

How the DIKWP model is applied to the Big Five personality traits, examining each trait in contrast and observing how each trait evolves on the continuum from data to intention.

Through this framework, we can observe how the DIKWP model is applied to different psychological traits and how these traits manifest through the continuum from data to intention. Each personality trait has its observations at the data level, which are analyzed and transformed into information. This information, in turn, is understood and internalized, leading to the development of knowledge. Wisdom comes into play in utilizing this knowledge to make ethical and value-based decisions, with the ultimate intention of translating these decisions into actions that align with personal goals and contribute to societal well-being.

This analysis provides a framework for exploring the roles of different personality traits in cognitive processing and how they influence individual behavior and decision-making. It highlights the complex interactions between the most basic data recording and the eventual behavioral intentions, showcasing how individuals use their cognitive resources to navigate and optimize their social and personal worlds.

For a more in-depth comparative analysis, additional data and research results would be required. This could involve psychological assessments, behavioral observations, long-term studies, and cross-cultural comparisons. However, in this overview, we can see that despite the DIKWP model originating in the field of information science, it is equally applicable to psychology and behavioral science. It serves as a useful theoretical tool for understanding and explaining the complexity of human behavior.

Applying the DIKWP model to the five major personality traits in psychology involves creating a system where each aspect of each trait (Data, Information, Knowledge, Wisdom, and Purpose) is represented by a set of mathematical functions capable of transforming inputs into outputs. Here, inputs and outputs correspond to the various stages of the DIKWP model.

To simplify this mathematical model, we can assume that the transformation at each level can be represented by a deterministic function. In reality, these functions may be more complex and subject to the influence of random variables, but for the purpose of this representation, we treat them as straightforward mappings.

The following is a modular view illustrating the internal resource transformation and processing within the DIKWP model and among personality traits:

1. Data to Information Transformation (D→I):

· Function f1: Observed behavioral data → Analysis of behavioral patterns

· Function f2: Specific details of task completion → Assessment of reliability and organization

· Function f3: Frequency of social activities → Evaluation of social skills and influence

· Function f4: Cooperative and conflict resolution behaviors → Analysis of empathy and cooperation

· Function f5: Emotional reactions and stress levels → Analysis of emotional fluctuations

2. Information to Knowledge Transformation (I→K):

· Function g1: Acceptance analysis of new experiences → Understanding the reasons behind behaviors

· Function g2: Assessment of reliability and organization → Knowledge of strategies to improve responsibility

· Function g3: Evaluation of social skills and influence → Understanding the connection between social behavior and relationships

· Function g4: Analysis of empathy and cooperation → Understanding behavioral patterns that promote harmonious social interaction

· Function g5: Analysis of emotional fluctuations → Understanding factors influencing emotional stability

3. Knowledge to Wisdom Transformation (K→W):

· Function h1: Understanding the reasons behind behaviors → Decision-making to enhance quality of life

· Function h2: Strategies to improve responsibility → Application of responsibility in the long term

· Function h3: Understanding social behavior and relationships → Decision-making to promote well-being in social activities

· Function h4: Understanding behaviors that promote harmonious interaction → Application of agreeableness in personal relationships and communities

· Function h5: Understanding factors influencing emotional stability → Strategies to manage emotional reactions

4. Wisdom to Purpose Transformation (W→P):

· Function j1: Decision-making to enhance quality of life → Actions to promote creativity and personal growth

· Function j2: Application of responsibility → Actions to pursue efficiency and an organized life

· Function j3: Decision-making to promote well-being → Actions for active participation in social networks

· Function j4: Application of agreeableness in relationships → Actions to build a harmonious social environment

· Function j5: Strategies to manage emotional reactions → Actions to achieve emotional stability

5. Interaction between Personality Traits (Cross-trait Interaction):

· Functions k1-5: Mutual influences and synergistic effects between openness, conscientiousness, extraversion, agreeableness, and neuroticism

In this model, each function represents a transformation process, converting the output of one level into the input of the next. For instance, function f1 transforms behavioral data under openness into information about the acceptance of new experiences. This transformation may involve statistical analysis, pattern recognition, or machine learning algorithms.

Constructing such a model helps us understand how each personality trait influences individual decision-making and behavior through the processing stages of the DIKWP model. While the precise form of each function may require detailed psychological research and data analysis in practical applications, this framework provides a structured mathematical approach to understanding personality traits.

We will analyze each personality trait through a series of modular processing steps, following the path from Data to Information to Knowledge to Wisdom to Purpose in the DIKWP model.

Openness

· Data (D): The data for openness includes an individual's concrete expressions of openness to new experiences, abstract thinking, and aesthetic preferences. Data points encompass choices, behaviors, reactions, and modes of expression.

· Information (I): Information is derived from data, such as the degree of acceptance of new experiences, evaluations of artistic works, and performance in open-minded thinking. Through comparison and analysis of these data, we can identify an individual's receptiveness to new ideas and tendencies toward creative thinking.

· Knowledge (K): Knowledge involves a deep understanding of the reasons and motivations behind an individual's open behavior. It includes transforming information into a comprehensive understanding of how individuals manifest openness in different environments, such as understanding how individuals absorb different viewpoints in a multicultural context.

· Wisdom (W): Wisdom is the application of knowledge about openness, guiding decision-making and behavior in life. This involves utilizing openness to foster innovation and solve complex problems.

· Purpose (P): The purpose of openness is to use wisdom to achieve specific goals, such as personal creative expression or promoting diversity and innovation within a team.

Conscientiousness

· Data (D): Data for conscientiousness includes an individual's daily organizational skills, planning ability, task completion, and attention to detail.

· Information (I): Extracted information from conscientiousness data may include work efficiency, punctuality, and the ability to maintain organization under pressure.

· Knowledge (K): Conscientiousness knowledge is an understanding of the consistency and predictability of an individual's conscientious traits. This may involve analyzing how individuals exhibit self-discipline and orderliness in different situations.

· Wisdom (W): The wisdom of conscientiousness lies in foreseeing the impact of behavior on the future and making reasonable judgments and decisions based on that foresight.

· Purpose (P): The purpose of conscientiousness is to achieve efficiency and an organized life through daily behavior, reaching personal and professional goals.

Extraversion

· Data (D): Extraversion data involves an individual's social activities, energy levels, and reactions to social situations.

· Information (I): Information is an analysis of extraversion data, such as the frequency of social activities, individual behavioral patterns in groups, and responses to external stimuli.

· Knowledge (K): Extraversion knowledge is an understanding of how individuals connect with others and function in social environments.

· Wisdom (W): The wisdom of extraversion is how to transform an individual's social energy into positive interpersonal relationships and community involvement.

· Purpose (P): The purpose of extraversion is to enhance personal and collective well-being through the utilization of social traits.

Agreeableness

· Data (D): Agreeableness data includes an individual's cooperative behavior, expressions of sympathy, and conflict resolution skills.

· Information (I): Information derived from agreeableness data may focus on the degree of collaboration in teams and how individuals handle social conflicts.

· Knowledge (K): Agreeableness knowledge is an in-depth understanding of how individuals exhibit altruism and sympathy in social interactions.

· Wisdom (W): The wisdom of agreeableness lies in how to use this trait to maintain and strengthen interpersonal relationships and community connections.

· Purpose (P): The purpose of agreeableness is to promote a more harmonious social environment through individual behavior and attitudes.

Neuroticism

· Data (D): Neuroticism data includes an individual's emotional changes, stress responses, and anxiety levels.

· Information (I): Information about neuroticism is an analysis of emotional data aimed at identifying patterns and triggers of emotional instability.

· Knowledge (K): Neuroticism knowledge is an understanding of the deep-rooted reasons behind an individual's emotional responses, including their emotional regulation abilities.

· Wisdom (W): The wisdom of neuroticism is how to manage and alleviate negative emotions and maintain composure and rationality under stress.

· Purpose (P): The purpose of neuroticism is to enhance quality of life and mental health by improving emotional management skills.

Differences and Connections between Personality Traits 

While each personality trait follows the transformation path from data to purpose, there are significant differences between them. For example, openness is related to creativity and the acceptance of new experiences, while conscientiousness is more associated with organization and efficiency. Extraversion is linked to social interaction, agreeableness focuses on interpersonal harmony, and neuroticism involves emotional stability. The connections between these traits lie in their collective formation of an individual's complex personality framework, influencing and determining how individuals process information, acquire knowledge, apply wisdom, and ultimately form action intentions.

Through this modular processing, the DIKWP model provides a powerful tool for understanding and analyzing individual personality traits. It emphasizes the transformation and value-addition of information, as well as how this process helps understand and predict individual behavior in different environments. By mapping psychological traits onto this model, we can better comprehend individual differences and provide a solid theoretical foundation for psychological assessments and behavioral predictions.

The following is a tabular comparison analysis, showing the application of the Big Five personality traits in the DIKWP model:

This table illustrates how each personality trait is analyzed and understood through each stage of the DIKWP model. Starting from data collection (records of behavior, choices, and reactions), moving on to information processing (analysis of behavior patterns), then forming knowledge (understanding the reasons behind behavior), followed by applying wisdom (decision-making based on knowledge), and finally achieving intent (goal-oriented actions). Through this structured approach, we can clearly see how each trait is processed and transformed at different stages, and their role and significance in the field of personality psychology.

The different levels of each trait interact with each other, collectively determining an individual's personality and behavior patterns. For example, openness may manifest as curiosity about new experiences at the data stage and transform into innovative and creative behavior at the intent stage. Conscientiousness, starting from daily organization and orderliness, leads to goal-oriented behavior and self-management. The differences between these traits are reflected in how they process and transform information and their ultimate impact on individual decision-making and behavior. By comparing how these traits are processed in each stage of the DIKWP model, we can gain a deeper understanding of the diversity of personalities and how they shape our interactions with the world.

As an algorithm engineer, utilizing the DIKWP model for automatic classification of the Big Five personality traits implies treating each stage of processing as part of data processing and feature engineering. In practical terms, you may use machine learning models to extract features (information) from raw data, train models to identify patterns (knowledge), ultimately use the model to predict personality traits (wisdom), and possibly implement these predictions in a user interface to serve specific purposes. The table below outlines how each DIKWP stage can be translated into algorithmic processing steps:

In this framework, each stage represents a step in the process of automated classification:

Data Processing: Collect raw data related to personality traits, such as psychological test results, records of daily activities, etc.

Information Extraction: Extract useful features from the raw data, reflecting key indicators of personality traits.

Knowledge Modeling: Use these features to train machine learning models, such as decision trees, support vector machines, or neural networks, to identify patterns of personality traits.

Wisdom Application: Apply these models to predict or recommend decisions or behaviors that align with the user's personality traits.

Intent Implementation: Integrate the prediction results into the user interface to achieve specific purposes such as personalized recommendations, productivity enhancement, or health interventions.

Through these steps, you can build a system that not only automatically classifies individual personality traits but also provides practical and personalized outputs based on these classifications. This process combines the methods of data science with psychological theories, creating a new tool to better understand and serve individual differences.

Understanding the Big Five personality traits (Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) for an algorithm engineer means constructing a classification system capable of identifying and distinguishing these personality traits. The table below uses the transformation and processing steps of the DIKWP model to define the basis for classifying each personality trait and illustrates the differences between these traits:

In this table, we have described each step from data collection to intent implementation and how each aspect of personality trait is classified. These steps are:

Data Processing: This is the initial stage of collecting relevant behaviors and psychological measurements. For instance, neuroticism can be measured through psychological questionnaires and emotional diaries, while openness might rely more on performance records in creative tasks.

Information Extraction: Key features are extracted from raw data using statistical and data analysis techniques. For example, agreeableness's cooperative tendencies and social harmony index can be obtained from team collaboration assessments and interpersonal relationship satisfaction surveys.

Knowledge Modeling: Machine learning algorithms are used to build models capable of recognizing behavioral patterns for each personality trait. For instance, a conscientiousness model might identify an individual's time planning and task completion abilities.

Wisdom Application: The models constructed are applied to predict or provide recommendations that can help individuals or organizations optimize decisions and behaviors. For example, an extraversion model might be used to offer advice on social activities and career development.

Intent Implementation: Finally, the outputs of these models are applied to design tools and applications to achieve specific purposes, such as enhancing individual mental health or strengthening team collaboration.

In this way, the DIKWP model not only helps algorithm engineers understand the nature of personality traits but also provides a practical framework for developing systems that can automatically identify these traits. The implementation of such a system will heavily depend on the accuracy and effectiveness of each stage's processing and transformation.

I will use the transformation and processing steps of the DIKWP model to elaborate on the automatic classification criteria for the Big Five personality traits. Each trait will be marked and differentiated through different data sources, information processing techniques, methods of knowledge extraction, applications at the wisdom level, and ways of intent implementation. The following table describes this process:

Duan Yucong, 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.

Distinction and Connection between Data and Information

Data is an objective description of the real world, representing records of facts such as numbers, text, and symbols. Data itself does not inherently carry direct significance, but it serves as the cornerstone for constructing information. From a mathematical perspective, data can be viewed as elements within a set, and these sets represent concrete representations of the "similar" semantics we comprehend. For instance, a set of temperature readings constitutes data, describing specific temperature values in a particular environment.

Information is the meaningful output of processed and organized data, representing expressions of "distinct" semantics within the data. In mathematical terms, information can be defined through the relationships and differences between data. For instance, the average, fluctuation range, or trend of temperature readings constitutes information, providing a cognitive understanding beyond mere data.

Formation and Application of Knowledge

Knowledge is the further refinement and understanding of information, achieved through connections, patterns, pattern recognition, and summarization of experiences, resulting in a profound insight into the world. In mathematical models, knowledge can be viewed as functions based on sets of information that can interpret the connections and principles behind the information. For instance, by analyzing temperature readings multiple times, we may deduce patterns in the temperature variations throughout the day, and this pattern constitutes knowledge.

The Nature and Application of Wisdom

Wisdom is the profound application of knowledge, considering aspects such as values, ethics, and morality. Wisdom is challenging to quantify mathematically, but it can be approximated through decision models. These models are based on knowledge and information while incorporating value judgments and anticipated goals. For example, wisdom might guide us to choose an appropriate air conditioning temperature to reduce energy consumption on a hot day.

The Significance and Processing of Purpose

Purpose, the ultimate output of the DIKWP model, represents our understanding (input) of specific phenomena and our goals (output). Mathematically, purpose can be modeled as a function or mapping, transforming the input DIKWP content into specific objectives or outcomes. For instance, in natural language processing, purpose recognition generates an output that satisfies user needs (such as performing a task or providing information) based on the user's input (instructions or queries).

The Objectivity and Subjective Refinement of Data

From an objective standpoint, data is a direct record of the real world, including raw numbers, text, and images obtained through observation. Subjective refinement of data involves the classification and interpretation of these raw records to impart specific meanings. For example, in meteorology, temperature and humidity readings are objective data, while associating these readings with specific weather phenomena is a form of subjective refinement.

Diversity and Semantic Reconstruction of Information

Information is the processing and organization of data, reflecting the diversity and distinctiveness inherent in the data. Objectively, information is a meaningful combination of data, such as the summarization of statistical data. On a subjective level, the semantic reconstruction of information involves further interpretation and understanding of these combinations, enabling us to identify and leverage the intrinsic value of the data.

Integrity and Wisdom Construction of Knowledge

Knowledge is a deeper understanding derived from the analysis, comparison, and reasoning based on information. Objectively, it manifests as a set of facts, principles, and patterns. From a subjective perspective, the intellectual construction of knowledge requires internalizing these facts and patterns into personal cognitive structures, guiding our behavior and decision-making.

Ethical Values and Judgment of Wisdom

Wisdom is the application and transformation of knowledge; it is not merely the accumulation of information and knowledge but, more importantly, the ability to use this knowledge to make insightful decisions. Objectively, wisdom is manifested as an efficient decision-making capability based on knowledge. On a subjective level, the ethical values and judgment of wisdom require us to consider morals and values in decision-making to achieve the maximum social and personal benefit.

Implementation of Purpose and Goal Orientation

Purpose is the ultimate goal of the DIKWP model, describing what we aim to achieve through the processing of information and knowledge. Objectively, purpose can be seen as a predetermined outcome or output. Subjectively, the implementation process of purpose requires us to apply wisdom in specific environments and situations to reach our goals.

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[30] Duan Y. Exploring GPT-4, Bias, and its Association with the DIKWP Model. DOI: 10.13140/RG.2.2.11687.32161. https://www.researchgate.net/publication/374420003_tantaoGPT-4pianjianjiqiyuDIKWPmoxingdeguanlian_Exploring_GPT-4_Bias_and_its_Association_with_the_DIKWP_Model. 2023.

[31] Duan Y. DIKWP language: a semantic bridge connecting humans and AI. DOI: 10.13140/RG.2.2.16464.89602. https://www.researchgate.net/publication/374385889_DIKWP_yuyanlianjierenleiyu_AI_deyuyiqiaoliang. 2023.

[32] Duan Y. The DIKWP artificial consciousness of the DIKWP automaton method displays the corresponding processing process at the level of word and word granularity. DOI: 10.13140/RG.2.2.13773.00483. https://www.researchgate.net/publication/374267176_DIKWP_rengongyishide_DIKWP_zidongjifangshiyiziciliducengjizhanxianduiyingdechuliguocheng. 2023.

[33] Duan Y. Implementation and Application of Artificial wisdom in DIKWP Model: Exploring a Deep Framework from Data to Decision Making. DOI: 10.13140/RG.2.2.33276.51847. https://www.researchgate.net/publication/374266065_rengongzhinengzai_DIKWP_moxingzhongdeshixianyuyingyongtansuocongshujudaojuecedeshendukuangjia_duanyucongYucong_Duan. 2023.

[34] Duan Y. DIKWP Digital Economics 12 Chain Machine Learning Chain: Data Learning, Information Learning, Knowledge Learning, Intelligent Learning, purposeal Learning. DOI: 10.13140/RG.2.2.26565.63201. https://www.researchgate.net/publication/374266062_DIKWP_shuzijingjixue_12_lianzhijiqixuexilian_shujuxuexi-xinxixuexi-zhishixuexi-zhihuixue_xi-yituxuexi_duanyucongYucong_Duan. 2023

[35] Duan Y. Big Data and Small Data Governance Based on DIKWP Model: Challenges and Opportunities for China. DOI: 10.13140/RG.2.2.21532.46724. https://www.researchgate.net/publication/374266054_jiyuDIKWPmoxingdedashujuyuxiaoshujuzhili_zhongguodetiaozhanyujiyu. 2023.

[36] Duan Y. DIKWP is based on digital governance: from "data governance", "information governance", "knowledge governance" to "wisdom governance". "Analysis of the current situation. DOI: 10.13140/RG.2.2.23210.18883. https://www.researchgate.net/publication/374265977_DIKWPjiyushuzizhilicongshujuzhilixinxizhilizhishizhilidaozhihuihuazhilidexianzhuangfenxi. 2023.

[37] Duan Y. Exploration of the nature of data tenure and rights enforcement issues based on the DIKWP model. DOI: 10.13140/RG.2.2.35793.10080. https://www.researchgate.net/publication/374265942_jiyu_DIKWP_moxingdeshujuquanshuxingzhiyuquequanwentitantao_duanyucongYucong_Duan. 2023.

[38] Duan Y. The DIKWP Model: Bridging Human and Artificial Consciousness. DOI: 10.13140/RG.2.2.23839.33447. https://www.researchgate.net/publication/374265912_DIKWP_moxingrenleiyurengongyishideqiaoliang_duanyucongYucong_Duan. 2023.

[39] Duan Y. An Exploration of Data Assetisation Based on the DIKWP Model: Definitions, Challenges and Prospects. DOI: 10.13140/RG.2.2.24887.91043. https://www.researchgate.net/publication/374265881_jiyu_DIKWP_moxingdeshujuzichanhuatanjiudingyitiaozhanyuqianjing_duanyucongYucong_Duan. 2023.

[40] Duan Y. Purpose-driven DIKWP Resource Transformation Processing: A New Dimension of Digital Governance. DOI: 10.13140/RG.2.2.29921.07529. https://www.researchgate.net/publication/374265796_yituqudongde_DIKWP_ziyuanzhuanhuachulishuzizhilidexinweidu_duanyucongYucong_Duan. 2023.

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