Application of the DIKWP Model in Automatic Classification of the Big Five Personality Traits

• November 2023

• DOI: 

• 10.13140/RG.2.2.13072.38408

• Yucong Duan

Application of the DIKWP Model in Automatic Classification 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

Abstract: This report aims to explore the application of the Data, Information, Knowledge, Wisdom, Purpose (DIKWP) model in automating the identification and classification of the Big Five personality traits: Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. We will detail the interaction of these traits with each stage of the DIKWP model and demonstrate how to design algorithms based on these interactions. An in-depth analysis of the transformations between the DIKWP stages will clarify how these traits’ unique processing paths can be utilized to construct a personality classification system.

Introduction

In psychology and personality studies, the identification and classification of the Big Five personality traits has always been a significant topic. With the advancement of artificial intelligence and machine learning technologies, we have the opportunity to automate this process. The DIKWP model provides a framework to understand how to process related data, extract information, build knowledge, apply wisdom, and ultimately form purpose-driven behavior.

The DIKWP model is an information processing framework comprising five stages: Data (D), Information (I), Knowledge (K), Wisdom (W), and Purpose (P). In this framework, raw data undergo various processing and transformation steps, ultimately converting into actions and decisions. Each stage corresponds to different processing levels from the original input to the final output.

Personality traits are long-term stable characteristics of individual behavior, emotion, and cognition patterns. Each trait can be defined and identified through specific processing and transformation paths in the DIKWP model. The main correlations between each trait and the DIKWP model are as follows:

Openness (O): The Openness trait involves an individual's innovativeness, curiosity, and openness to new experiences. In the DIKWP model, Openness is most closely associated with the transformation from Data to Information (D→I), as an individual's openness can be identified through analysis of novel events and abstract thinking test results.

Conscientiousness (C): The Conscientiousness trait relates to an individual's self-discipline, organization, and efficiency. This trait is most prominent in the transformation from Information to Knowledge (I→K), as conscientious individuals tend to convert task completion quality and efficiency information into reliable knowledge.

Extraversion (E): The Extraversion trait pertains to an individual's activeness and enthusiasm in social settings. The key transformation module for the Extraversion trait is Data to Information (D→I), as this trait can be identified by analyzing social activity records and social media interaction data.

Agreeableness (A): The Agreeableness trait involves an individual's cooperative inclination, altruism, and interpersonal sensitivity. In the DIKWP model, Agreeableness is closely related to the transformation from Information to Knowledge (I→K), especially in team cooperation assessment and interpersonal relationship satisfaction.

Neuroticism (N): The Neuroticism trait is related to an individual's emotional stability and stress management ability. This trait is weaker in the transformation from Knowledge to Wisdom (K→W), as individuals with high neuroticism may face challenges in applying knowledge to effective emotional regulation and stress management.

Transformation Modules in DIKWP and Personality Trait Identification：

The automatic classification of personality traits requires a deep understanding of each transformation module in the DIKWP model. The following is a detailed analysis of how to use the DIKWP model's transformation modules to differentiate and identify the Big Five personality traits:

Openness (O) Identification Process:

D→I: Open individuals often generate abundant data in novel and creative tasks, which need to be transformed into quantitative information regarding their preferences and interests.

I→K: When information transforms into knowledge, the Openness trait is evident in how individuals convert their preferences for novel things into knowledge of adaptability and creative solutions for different situations.

K→W: In transforming knowledge into wisdom, open individuals may propose innovative perspectives and solutions by integrating diverse ways of thinking.

W→P: When wisdom transforms into purpose, open individuals tend to apply creative thinking to practical decision-making and actions, such as promoting cross-cultural exchanges or participating in innovative projects.

Conscientiousness (C) Identification Process:

D→I: Data generated by conscientious individuals in task completion and daily planning need to be transformed into information about their organization and efficiency.

I→K: This information is then transformed into knowledge, reflecting in the ability of individuals to apply organization and efficiency to task completion and goal setting.

K→W: In the process of transforming knowledge into wisdom, conscientious individuals demonstrate how to use planning and organizational skills for practical problem-solving and risk management.

W→P: When transforming wisdom into purpose, conscientious individuals tend to develop and implement detailed plans to achieve personal and professional development

Establish a 5x5 DIKWP transformation module matrix, allowing each personality trait to be defined and identified through its unique Data (D), Information (I), Knowledge (K), Wisdom (W), Purpose (P) processing paths. This model will help us differentiate between the paths and focus differences of personality traits along the DIKWP processing chain.

For illustrative purposes, we define the following transformation module markers:

D→I: Transformation from Data to Information

D→K: Transformation from Data to Knowledge

D→W: Transformation from Data to Wisdom

D→P: Transformation from Data to Purpose

I→D: Feedback from Information to Data

I→K: Transformation from Information to Knowledge

I→W: Transformation from Information to Wisdom

I→P: Transformation from Information to Purpose

K→D: Feedback from Knowledge to Data

K→I: Feedback from Knowledge to Information

K→W: Transformation from Knowledge to Wisdom

K→P: Transformation from Knowledge to Purpose

W→D: Feedback from Wisdom to Data

W→I: Feedback from Wisdom to Information

W→K: Feedback from Wisdom to Knowledge

W→P: Transformation from Wisdom to Purpose

P→D: Feedback from Purpose to Data

P→I: Feedback from Purpose to Information

P→K: Feedback from Purpose to Knowledge

P→W: Feedback from Purpose to Wisdom

D↔I: Bidirectional interaction between Data and Information

D↔K: Bidirectional interaction between Data and Knowledge

D↔W: Bidirectional interaction between Data and Wisdom

D↔P: Bidirectional interaction between Data and Purpose

I↔K: Bidirectional interaction between Information and Knowledge

I↔W: Bidirectional interaction between Information and Wisdom

I↔P: Bidirectional interaction between Information and Purpose

K↔W: Bidirectional interaction between Knowledge and Wisdom

K↔P: Bidirectional interaction between Knowledge and Purpose

W↔P: Bidirectional interaction between Wisdom and Purpose

Using these transformation module markers, we now construct a table to analyze the differences in the DIKWP processing chain for the Big Five personality traits. This table will show the application of each DIKWP module in the definition and identification process of each personality trait, as well as the coverage differences of these modules between different traits.

Due to space constraints, we cannot show all 25 modules' correlation with the Big Five personality traits in the table. Therefore, we will focus on the most significant module transformations and processing differences for each trait. These key module transformations will reflect the core characteristics and different processing paths of each trait.

In this analysis, key module transformations in the definition and identification process for each trait are marked as "High," "Medium," or "Low," indicating the level of importance of that module in the processing of the trait.

Openness Trait (Openness):

The Openness trait emphasizes innovativeness and acceptance of novel experiences. Thus, the transformation from Data to Information (D→I) and Wisdom to Purpose (W→P) are marked as "High," indicating their critical role in identifying the Openness trait. Open individuals often generate a lot of data in novel situations and can transform wisdom into practical action innovatively.

The bidirectional interaction between Information and Knowledge (I↔K) is marked as "Medium" in the Openness trait, as these individuals need not only to acquire knowledge but also to feedback information to continually enhance their understanding and strategies for novel things.

Conscientiousness Trait (Conscientiousness):

The core of the Conscientiousness trait lies in the consistency and organizational ability in task execution. Therefore, the transformation from Information to Knowledge (I→K) and Wisdom to Purpose (W→P) are crucial, both marked as "High." Individuals with the Conscientiousness trait are adept at extracting useful knowledge from information and applying wisdom to achieve their long-term goals.

The bidirectional interaction between Data and Information (D↔I) is marked as "Medium," as these individuals often need to check their behavioral data and adjust their plans and organizational strategies accordingly.

Extraversion Trait (Extraversion):

For the Extraversion trait, the processing of social activity frequency and social media interaction data is particularly important, hence the transformation from Data to Information (D→I) is marked as "High." These types of individuals usually generate a lot of data in social activities, which needs to be transformed into meaningful information to understand their social behavior patterns.

The transformation from Knowledge to Wisdom (K→W) and Wisdom to Purpose (W→P) are marked as "Medium," as extraverted individuals have a moderate level of importance in applying social knowledge to building meaningful interpersonal relationships and community involvement.

Agreeableness Trait (Agreeableness):

The Agreeableness trait is prominently displayed in team cooperation and interpersonal relationship satisfaction, thus the transformation from Information to Knowledge (I→K) is marked as "High." Agreeable individuals tend to transform social information into knowledge about maintaining harmonious interpersonal relationships.

The transformation from Wisdom to Purpose (W→P) is marked as "Medium," indicating that agreeable individuals have a moderate importance in transforming their interpersonal relationship wisdom into actions to promote teamwork and social welfare.

Neuroticism Trait (Neuroticism):

Individuals with the Neuroticism trait face challenges in emotional fluctuations and stress management, thus the transformation from Knowledge to Wisdom (K→W) is marked as "Low." This means that these individuals may have difficulty transforming knowledge into effective wisdom to cope with daily challenges.

The transformation from Data to Information (D→I) is also marked as "Low," reflecting the challenge of extracting useful information from emotional and stress data.

In building algorithms for personality trait identification, these differences guide us to focus on the key transformation modules in the data processing chain for different traits. For instance, algorithms for the Openness trait might need to enhance the extraction of features related to innovative thinking and responses to novel experiences, while algorithms for the Conscientiousness trait might focus more on organizational ability and task completion efficiency. Algorithms for Extraversion might emphasize social interaction and network analysis, while those for Agreeableness and Neuroticism might need a deeper analysis of team interactions and emotional stability.

This analysis not only helps us more accurately identify individual personality traits but also enhances the efficiency of personalized applications and services, thus better meeting user needs. In designing these algorithms, it's important to consider how to effectively transform data into useful information, how to integrate this information into profound knowledge, and how to apply this knowledge in practical situations to achieve meaningful goals. Through a deep understanding and analysis of these modules, we can design more complex and refined algorithms that find valuable patterns in complex human behavioral data and provide better services for users.

We use the DIKWP model to mark and differentiate the processing and transformation processes of each trait. The following are case studies of the application of the DIKWP framework in this context:

Data Collection (Data):

First, we need to collect raw data about user behavior. For example:

Openness: User posts and comments on arts and cultural forums.

Conscientiousness: User activity on planning tools or scheduling apps.

Extraversion: User's social media activity frequency, including the number of posts and the size of the social network.

Agreeableness: User interactions in group discussions, such as the frequency of supportive comments.

Neuroticism: User participation in health and mental health forums.

Information Transformation (Information):

Next, we transform the raw data into information:

Openness: Identifying samples of the user's responses to novel events and creative expressions.

Conscientiousness: Assessing the user's efficiency in task completion and attention to detail.

Extraversion: Analyzing the breadth and depth of the user's interactions with others.

Agreeableness: Evidence of the user's empathetic and cooperative behavior.

Neuroticism: The user's expressed emotional stability and response to stress.

Knowledge Construction (Knowledge):

We then analyze the information to form a deep understanding of each user's personality traits:

Openness: Identifying the user's tendency to explore the unknown and solve problems.

Conscientiousness: Evaluating the user's organization and reliability in daily life.

Extraversion: Understanding the user's level of activity and influence in social settings.

Agreeableness: Assessing the user's cooperativeness and conflict resolution skills in teams.

Neuroticism: Identifying patterns in the user's emotional responses and coping strategies.

Wisdom Application (Wisdom):

Based on the knowledge gained, we design algorithms to predict or recommend actions:

Openness: Recommending new creative activities and cultural experiences to the user.

Conscientiousness: Providing personalized time management and productivity enhancement tips.

Extraversion: Enhancing the user's social network and public engagement.

Agreeableness: Facilitating the user's teamwork and social harmony.

Neuroticism: Offering customized stress management and emotional regulation tools.

Purpose Realization (Purpose):

Finally, we transform wisdom-level applications into specific functionalities in the user interface:

Openness: Developing a platform that recommends personalized art exhibitions and creative workshops.

Conscientiousness: Creating an app to help users optimize their scheduling and task management.

Extraversion: Designing a social media tool to help users expand their influence and social circle.

Agreeableness: Implementing a team assessment tool to enhance collaboration and empathy in the workplace.

Neuroticism: Developing a mental health app to monitor users' emotional states and provide immediate support.

In constructing the algorithm, we ensure that each transformation and processing step is effectively adapted to different personality traits. This involves not only feature engineering but also a deep understanding of user behavior patterns and how to transform these patterns into useful applications and services. For instance:

For the Openness trait, algorithms might use natural language processing to analyze the user's language use, looking for signs of creativity and open-mindedness. These insights can then be used to develop recommendation systems that suggest new activities the user might like.

For the Conscientiousness trait, algorithms might track users' task completion records and time management habits, then transform this information into personalized productivity reports to help users better plan their schedules.

For the Extraversion trait, algorithms might analyze users' social network activities, such as the frequency and breadth of their interactions with other users and the feedback on their posts. This information can be used to measure users' social influence and provide suggestions to enhance it.

For the Agreeableness trait, algorithms might assess users' cooperative behaviors and interpersonal interactions, such as their roles in team projects and how they respond to team members. Then, this information can be used to provide training in cooperation skills and tools to enhance team collaboration.

For the Neuroticism trait, algorithms might monitor users' displayed emotional stability, such as their emotional expressions on social media and responses to stressful events. Then, this information can be used to offer tailored mental health support and stress management advice.

Through this approach, algorithms can not only identify different personality traits but also provide customized services and features for users with each trait. This personalized approach can increase user engagement, improve user satisfaction, and bring higher value to the platform in the long run. Moreover, by continually learning and adapting to users' behaviors, these algorithms can evolve over time to provide more accurate predictions and targeted recommendations.

The DIKWP framework provides a powerful tool for algorithm engineers to build complex personality classification systems. By understanding and leveraging the transformations and processing differences between Data, Information, Knowledge, Wisdom, and Purpose, we can develop highly personalized applications and services that recognize and respond to different personality traits. This method not only increases the precision and effectiveness of algorithms but also provides users with a richer and more satisfying experience.

Conclusion

This report demonstrates how to use the transformation and processing modules of the DIKWP model to automate the identification and classification of the Big Five personality traits. We highlighted the unique paths of each personality trait in the DIKWP processing chain and proposed how to design algorithms along these paths. Through an in-depth analysis of each trait's processing and transformation in the DIKWP model, we can better understand the nature of personality traits and lay the groundwork for developing more accurate personality classification tools.

This technical report provides a detailed framework that can help algorithm engineers design and implement efficient personality classification systems applicable in various fields such as personalized recommendations, career planning, team building, and mental health management. By proper data handling, precise information extraction, profound knowledge construction, effective wisdom application, and clear purpose realization, we can enhance the accuracy of personality trait classification, thereby better serving individual and societal needs.

This approach to personality trait analysis and classification using the DIKWP model offers a comprehensive and nuanced understanding of individual differences. By recognizing and addressing these differences, we can create more personalized and effective digital interactions. This not only benefits individuals by providing them with tailored experiences and insights into their own behavior but also offers organizations and businesses valuable tools for understanding and catering to their audiences or employees more effectively.

In summary, the application of the DIKWP model in the field of personality trait analysis represents a significant advancement in the realm of artificial intelligence and human-computer interaction. It exemplifies how a systematic approach to data processing can lead to deeper insights and more impactful applications in various domains. The integration of this model in the development of AI systems and applications paves the way for more intuitive, responsive, and personalized technologies that understand and adapt to human variability.

This report underscores the potential of the DIKWP model as a framework for developing sophisticated AI systems that not only process vast amounts of data but also translate this data into meaningful and actionable insights about human personality traits. As we continue to explore and refine these models, we can expect to see increasingly sophisticated and nuanced applications of AI in understanding and interacting with human users, ultimately leading to more personalized and effective digital experiences.

Detailed Case Study: Application of the DIKWP-AC System in Identifying and Classifying the Big Five Personality Traits

Background

The DIKWP-AC system leverages social media behavior analysis to identify the Big Five personality traits in users. The system analyzes posts, interactions, and activity data of users, then employs machine learning algorithms to provide personalized feedback and suggestions.

User Case Study

Imagine our user, Sara, an active social media user interested in self-awareness and personal development. She wants to understand her personality traits to better guide her career development and social activities.

Openness

Data (D): Sara frequently shares her travel experiences and reading insights on social media. Her posts include discussions on artworks and contemplation on philosophical questions.

Information (I): The DIKWP-AC system analyzes the content and frequency of Sara’s posts, focusing on keywords and topics related to culture and creativity.

Knowledge (K): The system uses natural language processing (NLP) algorithms to identify signs of Openness, such as innovative thinking and exploration of complex concepts.

Wisdom (W): The system assesses Sara's level of Openness based on her post content and engagement, generating an Openness profile, including her receptiveness to new experiences and display of creativity.

Purpose (P): Based on Sara’s Openness profile, the system recommends she participates in creative writing workshops and introduces her to new cultural activities and resources to enhance her open-minded thinking.

Conscientiousness

Data (D): Sara uses social media's scheduling features to plan her social activities and regularly updates completed tasks and goals.

Information (I): PersonalityInsight tracks Sara's schedule and task updates, analyzing her organizational and time management skills.

Knowledge (K): The system builds a knowledge base of her Conscientiousness by assessing the consistency of her plans and completion of her schedule.

Wisdom (W): The system provides Sara with a range of tools and tips to improve efficiency and advice on maintaining organization under pressure.

Purpose (P): Based on her Conscientiousness trait, the system plans a career development path for Sara, including milestones and timelines for achieving personal goals.

Extraversion

Data (D): Sara’s activities on social media include participating in social events and online interactions, like comments, likes, and shares.

Information (I): The system analyzes Sara's social network, including the number of friends, types of interactions, and activity level on social media.

Knowledge (K): DIKWP-AC identifies Sara's social patterns, such as how she builds relationships and influences others online.

Wisdom (W): The system provides strategies for Sara to enhance her social skills and expand her network based on her social behavior.

Purpose (P): The system recommends Sara become an organizer of social events and provides tools to enhance her social influence.

Agreeableness

Data (D): Sara often participates in charitable activities and community service on social media, shows appreciation for teamwork, and actively resolves disputes.

Information (I): DIKWP-AC collects Sara's behavior and responses in cooperative environments and her attitude towards social issues.

Knowledge (K): The system builds a profile of Sara's Agreeableness, assessing her altruism and empathy in interpersonal interactions.

Wisdom (W): Utilizing this profile, the system provides Sara with advice on establishing positive interpersonal relationships in her social and professional network.

Purpose (P): The system encourages Sara to participate in team leadership training and provides conflict resolution tools for her team members.

Neuroticism

Data (D): Sara sometimes shares her stress and anxiety on social media, especially when facing important decisions or difficult situations.

Information (I): The system analyzes Sara’s behavior patterns when facing challenges, including her emotional expressions and reactions to stress.

Knowledge (K): DIKWP-AC assesses Sara’s emotional stability in various situations, identifying key factors in stress management.

Wisdom (W): The system offers Sara customized emotional support suggestions, such as meditation and mindfulness exercises, to better manage stress.

Purpose (P): Based on Sara’s needs, the system recommends a range of stress-relief activities and mental health resources, along with professional support services.

Through this case study, we see how the DIKWP-AC system utilizes various aspects of the DIKWP model in the process of identifying and classifying user personality traits. This system not only identifies users' personality traits but also provides personalized feedback and suggestions, helping them better present themselves on social media and achieve personal development goals. Furthermore, the system continually learns and adapts through a feedback loop, constantly improving its algorithms to more accurately meet user needs.

The DIKWP model provides a robust framework for developing complex personalized services. By deeply understanding the unique data, information, knowledge, wisdom, and purpose processing paths of each personality trait, we can design more refined and effective algorithms. These algorithms can find valuable patterns in complex human behavior data and provide better services to users. This approach not only increases the accuracy and effectiveness of algorithms but also provides users with a richer and more satisfying experience.

Conclusion

This report has demonstrated the use of the DIKWP model's transformation and processing modules for automating the identification and classification of the Big Five personality traits. We emphasized the unique paths of each personality trait in the DIKWP processing chain and proposed how algorithms can be designed along these paths. Through an in-depth analysis of each trait's processing and transformation within the DIKWP model, we gained a better understanding of the nature of personality traits, laying the foundation for the development of more precise personality classification tools.

This technical report offers a detailed framework that assists algorithm engineers in designing and implementing efficient personality classification systems. These systems can be applied across various domains, including personalized recommendations, career planning, team building, and mental health management. By ensuring proper data handling, precise information extraction, profound knowledge construction, effective wisdom application, and clear purpose realization, we can enhance the accuracy of personality trait classification, thus better serving individual and societal needs.

Overall, this report highlights the potential of the DIKWP model as a tool for creating sophisticated, personalized services that can navigate the complexities of human behavior. This approach not only enhances the precision and effectiveness of the algorithms but also enriches the user experience, making it more comprehensive and satisfying. As we continue to explore and refine these models, we can expect increasingly sophisticated applications of AI in understanding and interacting with human users, leading to more personalized and effective digital experiences.

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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[21] Wang Y, Duan Y, Wang M, et al. Resource Adjustment Processing on the DIKWP Artificial Consciousness Diagnostic System, DOI: 10.13140/RG.2.2.23640.06401. https://www.researchgate.net/publication/375492685_Resource_Adjustment_Processing_on_the_DIKWP_Artificial_Consciousness_Diagnostic_System. 2023.

[22] Tang F, Duan Y, Wei J, et al. DIKWP Artificial Consciousness White Box Measurement Standards Framework Design and Practice, DOI: 10.13140/RG.2.2.23010.91848. https://www.researchgate.net/publication/375492522_DIKWP_Artificial_Consciousness_White_Box_Measurement_Standards_Framework_Design_and_Practice. 2023.

[23] Wu K, Duan Y, Chen L, et al. Computer Architecture and Chip Design for DIKWP Artificial Consciousness, DOI: 10.13140/RG.2.2.33077.24802. https://www.researchgate.net/publication/375492075_Computer_Architecture_and_Chip_Design_for_DIKWP_Artificial_Consciousness. 2023.

[24] Duan Y. Which characteristic does GPT-4 belong to? An analysis through DIKWP model. DOI: 10.13140/RG.2.2.25042.53447. https://www.researchgate.net/publication/375597900_Which_characteristic_does_GPT-4_belong_to_An_analysis_through_DIKWP_model_GPT-4_shishenmexinggeDIKWP_moxingfenxibaogao. 2023.

[25] Duan Y. DIKWP Processing Report on Five Personality Traits. DOI: 10.13140/RG.2.2.35738.00965. https://www.researchgate.net/publication/375597092_wudaxinggetezhide_DIKWP_chulibaogao_duanyucongYucong_Duan. 2023.

[26] Duan Y. Research on the Application of DIKWP Model in Automatic Classification of Five Personality Traits. DOI: 10.13140/RG.2.2.15605.35047. https://www.researchgate.net/publication/375597087_DIKWP_moxingzaiwudaxinggetezhizidongfenleizhongdeyingyongyanjiu_duanyucongYucong_Duan. 2023.

[27] Duan Y, Gong S. DIKWP-TRIZ method: an innovative problem-solving method that combines the DIKWP model and classic TRIZ. DOI: 10.13140/RG.2.2.12020.53120. https://www.researchgate.net/publication/375380084_DIKWP-TRIZfangfazongheDIKWPmoxinghejingdianTRIZdechuangxinwentijiejuefangfa. 2023.

[28] Duan Y. The Technological Prospects of Natural Language Programming in Large-scale AI Models: Implementation Based on DIKWP. DOI: 10.13140/RG.2.2.19207.57762. https://www.researchgate.net/publication/374585374_The_Technological_Prospects_of_Natural_Language_Programming_in_Large-scale_AI_Models_Implementation_Based_on_DIKWP_duanyucongYucong_Duan. 2023.

[29] Duan Y. The Technological Prospects of Natural Language Programming in Large-scale AI Models: Implementation Based on DIKWP. DOI: 10.13140/RG.2.2.19207.57762. https://www.researchgate.net/publication/374585374_The_Technological_Prospects_of_Natural_Language_Programming_in_Large-scale_AI_Models_Implementation_Based_on_DIKWP_duanyucongYucong_Duan. 2023.

[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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