博文
Semantic Mathematics to Help Traditional Publishing Industry
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Traditional 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)
Strategies of Semantic Mathematics to Help Traditional Publishing Industry to Meet the Challenges of AI
Yucong Duan, 
Shiming Gong
DIKWP-AC Artificial Consciousness Laboratory
AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory
World Association of Artificial Consciousness
(Email：duanyucong@hotmail.com)
Catalogue
3 Challenges faced by traditional publications
4 Transformation framework of semantic mathematics
With the rapid development of artificial intelligence (AI) technology, the processing ability of human data, information, knowledge, wisdom and purpose (DIKWP) is experiencing an unprecedented accelerated integration. Especially in the field of publishing, the traditional mechanism is facing severe challenges because of its inefficient handling and communication channels. This report deeply discusses the influence of AI technology on the traditional publishing industry, and analyzes the challenge of the widespread application of new technologies such as Agent to the existence and effectiveness of traditional publications. With the help of the core principles and transformation framework of semantic mathematics, the report puts forward the strategies for the traditional publishing industry to deal with the challenges of AI technology during the transformation from conceptual space to semantic space. By discussing in detail how to use semantic mathematics to enhance the semantic processing of content, improve the accuracy of personalized recommendation of content, build a dynamic updating mechanism, and strengthen copyright management and protection, the report provides specific strategies and directions for the transformation and upgrading of traditional publishing industry. This report not only explains the importance of semantic mathematics in the progress of modern science and technology, but also shows how it can help traditional industries adapt to the changes in the digital age, especially in improving the accuracy and efficiency of decision-making and promoting industry innovation. Through the in-depth application of semantic mathematics, the traditional publishing industry is expected to reshape and develop itself under the challenge of AI technology, and move towards a more intelligent and personalized future.
In the digital age, the rapid development and wide application of artificial intelligence (AI) technology, especially the progress in processing data, information, knowledge, wisdom and purpose (DIKWP), has brought unprecedented challenges and opportunities to the traditional publishing industry. The purpose of this paper is to deeply discuss the influence of AI technology, especially the widespread use of Agent, on traditional publications, and how to use the transformation framework of semantic mathematics to obtain content from conceptual space to semantic space to meet these challenges.
With the progress of AI technology, especially the wide application of Agent technology, the way of information acquisition, processing and dissemination has changed fundamentally. Agent technology makes information retrieval more personalized and intelligent, and users can easily obtain highly relevant and customized content. This change directly affects the value and position of traditional publications, because the static and consistent information dissemination mode of traditional publications can not meet the individual needs of users. In addition, AI technology also makes content creation, editing and distribution more efficient and low-cost, thus intensifying the challenge to the traditional publishing industry.
With the rapid development of artificial intelligence technology and the accelerated integration of DIKWP (data, information, knowledge, wisdom and purpose) model, traditional publishing is facing unprecedented challenges. The widespread use of AI technology, especially AI Agent, provides a faster way for human beings to obtain content from conceptual space to semantic space, which means that the traditional publishing industry needs to rethink its positioning and strategy in the new technical environment.
3 Challenges faced by traditional publications
Automation of content creation and planning: AI technology can automatically generate or plan content, which poses a challenge to the traditional publishing model that relies on editors and authors' manual creation. AI can not only improve the efficiency of content generation, but also provide personalized content recommendation according to users' reading preferences, which puts pressure on traditional publications in terms of content innovation and personalized service.
Transformation of distribution channels: With the rise of digital reading platform and social media, people's access to information is becoming more and more diversified. The distribution channels of traditional publications (such as physical bookstores and libraries) are gradually losing their dominant position, which requires traditional publishers to embrace digitalization and develop new online distribution strategies to adapt to the changes in consumer behavior.
Challenges of copyright and intellectual property rights: The application of AI technology, especially AI-generated content, has brought new challenges to copyright protection. How to define the copyright ownership of AI creative content and how to protect the intellectual property rights of traditional authors has become an important issue for traditional publishing industry.
The need to enhance readers' interaction and participation: Modern readers expect a more interactive and participatory reading experience. AI technologies, such as chat bots and augmented reality, provide new ways to enhance readers' participation. Traditional publishers need to explore these new technologies to provide more attractive reading products and experiences.
Increased demand for personalized content: With the development of technology, readers' demand for personalized content is increasing. AI and big data analysis can help publishers better understand readers' preferences and provide customized reading experience. For traditional publishers, how to use these technologies to meet individual needs is the key to enhance their competitiveness.
4 Transformation framework of semantic mathematics
One of the core goals of semantic mathematics is to realize the transformation of DIKWP model from conceptual space to semantic space, which provides a new idea for traditional publishing. Through in-depth analysis and understanding of the semantics behind the data, semantic mathematics can help the publishing industry better understand the needs of users and improve the relevance and attractiveness of the content.
Semanticization of data and information: Through semantic mathematics, data and information in publications can be endowed with richer and more accurate semantics, thus improving the quality and value of content.
Reconstruction of knowledge and wisdom: Using semantic mathematics to build a knowledge system can help publications better organize and present complex information and improve users' understanding and absorption ability.
Clarification of purpose: By clarifying the goal and purpose of content, publications can meet the needs of users more accurately and enhance the user experience.
Accelerate digital transformation: Traditional publishing industry should accelerate digital transformation and improve the efficiency of content creation, editing and distribution by using AI technology.
Improve the personalization and interactivity of content: develop personalized and interactive digital publishing products by using the framework of semantic mathematics to meet the personalized needs of users.
Establish a dynamic updating mechanism: establish a dynamic updating mechanism of content to ensure that publications can quickly respond to market changes and user needs.
Strengthen copyright management and protection: with the help of AI technology, strengthen copyright management and protection mechanism to ensure the originality and uniqueness of published content.
Facing the challenges brought by AI technology, the traditional publishing industry needs constant innovation and adaptation. Using the transformation framework of semantic mathematics, the content acquisition means from conceptual space to semantic space can provide new development direction and strategy for traditional publishing and realize the transformation and upgrading of the industry.
随着人工智能（AI）技术的飞速发展，人类的数据、信息、知识、智慧和意图（DIKWP）处理能力正在经历前所未有的加速融合。特别是在出版发行领域，传统机制因其低效的办理和传播渠道正面临严峻挑战。本报告深入探讨了AI技术对传统出版发行业带来的影响，分析了Agent等新技术的普遍应用对传统出版物存在性和有效性的挑战。报告借助语义数学的核心原理和转化框架，从概念空间到语义空间的转换过程中，提出了传统出版发行业应对AI技术挑战的策略。通过详细论述如何利用语义数学的方法来增强内容的语义化处理、提升内容的个性化推荐精准度、构建动态更新机制，以及加强版权管理和保护，报告为传统出版发行业的转型升级提供了具体的策略和方向。本报告不仅阐释了语义数学在现代科技进步中的重要性，还展示了其如何帮助传统行业适应数字化时代的变革，特别是在提高决策的准确性、效率和促进行业创新方面的作用。通过对语义数学的深入应用，传统出版发行业可望在AI技术的挑战下实现自身的重塑和发展，迈向更加智能化和个性化的未来。
在数字化时代，人工智能（AI）技术的快速发展和广泛应用，特别是在处理数据、信息、知识、智慧及意图（DIKWP）方面的进步，为传统出版发行业带来了前所未有的挑战和机遇。本文旨在深入探讨AI技术在加速DIKWP融合过程中，特别是Agent的普遍使用对传统出版物的影响，以及如何利用语义数学的转化框架从概念空间到语义空间的内容获取手段，来应对这些挑战。
随着AI技术的进步，尤其是Agent技术的广泛应用，信息获取、处理和传播的方式发生了根本性变化。Agent技术使得信息检索更加个性化和智能化，用户可以轻松获得高度相关和定制化的内容。这一变化直接影响了传统出版物的价值和地位，因为传统出版的静态、一致性的信息传播模式难以满足用户的个性化需求。此外，AI技术还使得内容创作、编辑和分发变得更加高效和低成本，从而加剧了对传统出版业的挑战。
人工智能技术的大幅发展和DIKWP（数据、信息、知识、智慧、意图）模型的加速融合，传统出版发行面临着前所未有的挑战。AI技术，特别是AI Agent的普遍使用，为人类提供了更快的跨越从概念空间到语义空间的内容获取手段，这对传统出版业意味着需要重新思考其在新的技术环境中的定位和策略。
内容创作和策划的自动化：AI技术能够自动生成或策划内容，这对传统依赖编辑和作者手工创作的出版模式构成了挑战。AI不仅能够提高内容生成的效率，还能够根据用户的阅读偏好提供个性化的内容推荐，这使得传统出版物在内容的创新性和个性化服务方面面临压力。
分发渠道的转变：随着数字化阅读平台和社交媒体的兴起，人们获取信息的途径越来越多元化。传统出版物的分发渠道（如实体书店和图书馆）正逐渐失去其主导地位，这要求传统出版商必须拥抱数字化，开发新的在线分发策略，以适应消费者行为的变化。
版权和知识产权的挑战：AI技术的运用，尤其是AI生成内容，给版权保护带来了新的挑战。如何界定AI创作内容的版权归属，以及如何保护传统作者的知识产权，成为传统出版业需要面对的重要问题。
读者互动和参与的提升需求：现代读者期待更加互动和参与的阅读体验。AI技术，如聊天机器人和增强现实，提供了增强读者参与度的新途径。传统出版商需要探索这些新技术，以提供更富吸引力的阅读产品和体验。
个性化内容的需求增加：随着技术的发展，读者对个性化内容的需求日益增长。AI和大数据分析能够帮助出版商更好地了解读者偏好，提供定制化的阅读体验。对于传统出版商而言，如何利用这些技术来满足个性化需求，是提升竞争力的关键。
语义数学的核心目标之一是实现DIKWP模型从概念空间到语义空间的转换，为传统出版发行提供了一种新的思路。通过深入分析和理解数据背后的语义，语义数学可以帮助出版业更好地理解用户需求，提高内容的相关性和吸引力。
数据与信息的语义化：通过语义数学，出版物中的数据和信息可以被赋予更加丰富和精确的语义，从而提高内容的质量和价值。
知识与智慧的重构：利用语义数学构建知识体系，可以帮助出版物更好地组织和呈现复杂信息，提高用户的理解和吸收能力。
意图的明确化：通过明确化内容的目标和意图，出版物可以更精准地满足用户的需求，提升用户体验。
加快数字化转型：传统出版业应加快数字化转型，利用AI技术提高内容创作、编辑和分发的效率。
提高内容的个性化和互动性：利用语义数学的框架，开发个性化和互动性强的数字化出版产品，以满足用户的个性化需求。
构建动态更新机制：建立内容动态更新机制，确保出版物能够快速响应市场变化和用户需求。
加强版权管理和保护：在AI技术的帮助下，加强版权管理和保护机制，确保出版内容的原创性和独特性。
面对AI技术带来的挑战，传统出版发行业需要不断创新和适应。利用语义数学的转化框架，从概念空间到语义空间的内容获取手段，可以为传统出版发行提供新的发展方向和策略，实现行业的转型升级。
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
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.
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, Director of Network Security and Informatization Association of Hainan Province, Director of Artificial Intelligence Society of Hainan Province, 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.
Prof. Yucong Duan
DIKWP-AC Artificial Consciousness Laboratory
AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory
DIKWP research group, Hainan University
duanyucong@hotmail.com
https://blog.sciencenet.cn/blog-3429562-1421914.html
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