Research Report on Innovation Establishment and Pricing in the

Research Report on Innovation Establishment and Pricing in the Future Semantic-Enabled Digital World

Yucong Duan

International Standardization Committee of Networked DIKWPfor Artificial Intelligence Evaluation(DIKWP-SC)

World Artificial Consciousness CIC(WAC)

World Conference on Artificial Consciousness(WCAC)

(Email: duanyucong@hotmail.com)

1. Conceptual Foundations: Semantic Definition of Innovation, Forms of Innovative Behavior in the Digital World, and Principles of Semantic Value Generation

In the future semantic-enabled digital world, the interconnection of all things is not just about data exchange but also about deep connections at the semantic level. The semantic definition of innovation can be understood as introducing a completely new and meaningful combination of semantics or knowledge structure into a digital environment to produce new value. This means that innovation is no longer limited to new physical inventions but extends to the creation and application of new concepts and knowledge within the semantic space. Based on the theoretical framework proposed by Professor Yucong Duan, innovation can be defined as introducing an unprecedented semantic combination within the five-layer semantic system of Data-Information-Knowledge-Wisdom-Purpose (DIKWP), thereby generating new knowledge or wisdom that serves a clear purpose or need. This definition emphasizes the semantic essence of innovation: innovation is the recombination and sublimation of semantic content, refining information from data, generating knowledge from information, cultivating wisdom from knowledge, and ultimately pointing towards a specific purpose and value pursuit of humanity or society. Therefore, innovative behavior can be seen as a creative leap at the semantic level—creating new nodes and relationships within the existing semantic network and endowing them with value and meaning.

In the digital world, the forms of innovative behavior are increasingly diverse and highly semanticized. For example, in AI-driven content creation, AI can collaborate with humans to generate novels, music, and design proposals, which are essentially innovative combinations of semantic content. In open-source software communities, developers share code and knowledge, leading to new software functions or technical solutions through semantic collaboration and improvement. Similarly, in data-driven research, different researchers share and recombine each other's data and conclusions, and the collision at the semantic level sparks new theoretical insights. These examples demonstrate that innovation in the digital age is more about creating intangible knowledge and content—the production of semantic assets. Compared to the traditional industrial era, which focused on tangible product innovation, innovation in today's digital economy places a greater emphasis on the generation and application of semantic assets such as data, information, and knowledge. As research indicates, modern economic operations can be viewed as the interaction and transformation of five elements: data, information, knowledge, wisdom, and purpose. Organizations and individuals create value by continuously transforming data into information, refining it into knowledge, forming wisdom, and using it to achieve specific purposes. This definition reveals the principle of semantic value generation: the creation of value stems from the progressive upgrading of semantic content along the DIKWP hierarchy. When a raw idea (data) is given context to become information, then structured into reusable knowledge, and further sublimated through comprehensive judgment into wisdom (insights that can guide decision-making), and finally serves a clear purpose or goal, the value density of the content increases with each upward leap in the semantic hierarchy. Research by Professor Yucong Duan's team points out that this evolutionary process from data to wisdom and then to purpose is the core mechanism of semantic value generation: each layer of processing adds new value elements to the semantic content, making higher-level semantic units embody greater innovative value. For example, an innovation might originate from the collection of massive amounts of data, but its value is not only reflected in the scarcity of the data itself but also in the novelty of the information extracted from it, the practicality of the knowledge synthesized from the information, and the impact of the wise decisions based on that knowledge. Ultimately, if this innovation can align with a certain social need or strategic goal (at the purpose level), its semantic value is fully realized.

Furthermore, we need to recognize that semantic value is not an entirely subjective or unquantifiable concept. On the contrary, in the semantic-enabled digital world, the semantic value of innovation can be measured from multiple dimensions. Traditionally, the value of data or innovative outcomes was often assessed based on quantity or scarcity, which overlooks the importance of semantic quality and knowledge content. The latest viewpoints suggest that a system of semantic value metrics can be established to provide a reference for pricing innovation. For example: semantic completeness (the extent to which an innovation covers relevant semantic elements), information novelty (the uniqueness of the information provided relative to existing knowledge), and knowledge relevance (the breadth and depth of the connection between new knowledge and the existing knowledge network). These metrics can be quantified as scores for the market to evaluate the value of innovative outcomes. For instance, a digital content innovation (such as an AI-generated educational course) may contain rich and complete semantic tags (high semantic completeness), offer new perspectives not previously proposed (high information novelty), and integrate well with existing teaching knowledge systems (high knowledge relevance). Such an innovation possesses high value at the semantic level. In summary, the principle of semantic value generation emphasizes that the value of innovation comes from the creative increment of semantic content and its alignment with the audience's cognition and target needs. The more unique, meaningful, and aligned with human purpose the semantic elements of an innovation are, the greater the value it creates.

2. Semantic Space Modeling: Innovation's Semantic Trajectory and Value Mapping Path in the DIKWP×DIKWP Interaction Model

To deeply characterize the trajectory of innovation in semantic space, we need to construct a semantic space model. The DIKWP×DIKWP interaction model proposed by Professor Yucong Duan's team provides a powerful theoretical tool for this purpose. This model can be understood as the docking and interaction of two DIKWP semantic systems, used to simulate the two-way mapping between conceptual space and semantic space. Simply put, the DIKWP×DIKWP model allows two entities or systems that follow the Data-Information-Knowledge-Wisdom-Purpose architecture to connect with each other: one end might represent innovative ideas at the conceptual level (such as the conceptual space in the human mind), and the other end represents the semantic space of a machine or digital platform. Through interactive mapping, the symbolized innovative ideas in the conceptual space are translated layer by layer into content that the other semantic system can "understand," thus achieving semantic connectivity. Professor Yucong Duan points out that by adding the "Purpose" layer, the DIKWP model enables AI systems not only to process abstract conceptual symbols but also to understand the motivations and goals behind the symbols, achieving a leap from conceptual space to semantic space. This is precisely the logic of semantic cognitive evolution—data is refined into knowledge through information, then sublimated into wisdom and imbued with purpose, thereby transforming human concepts into machine-understandable and executable semantic content. Therefore, when an innovation moves from its inception (conceptual level) to its adoption by a digital system (semantic level), the path it follows can be depicted by the DIKWP×DIKWP model: the innovative concept undergoes an ascent through the DIKWP chain at the source (forming its own semantic expression), and then through model mapping, it enters the DIKWP chain of the target system, where it is correctly interpreted and absorbed.

To more intuitively understand the semantic trajectory and value mapping path of innovation, we can view the DIKWP×DIKWP model as a 5×5 matrix structure: the horizontal and vertical axes represent the DIKWP levels of the innovator and the recipient, respectively. During the dissemination of an innovation, its semantic content is gradually sublimated from the data layer to the purpose layer on the sender's side, then mapped to the recipient's side through the interaction model, and finally broken down from the purpose/wisdom layer into specific knowledge and information, ultimately affecting its data layer (e.g., triggering new data feedback). In this process, we can trace the trajectory of the innovation's semantic content: for example, a scientific discovery first appears as experimental data (sender's data layer), is analyzed by researchers to form theoretical information and knowledge (sender's information and knowledge layers), rises to an insight into scientific laws (sender's wisdom layer), and aligns with the research goal (purpose layer). When this innovation is published through a paper or patent, the recipient's semantic system (the academic community or industry) receives this content: they first acquire the raw information such as the paper's text (recipient's data/information layer), understand the knowledge principles within (recipient's knowledge layer), integrate it into their own decision-making and practices (recipient's wisdom layer), and use it for their own purposes (recipient's purpose layer). Through the DIKWP×DIKWP interactive mapping, we can clearly depict the flow of innovation in different subjects' semantic spaces and how value is mapped and accumulated at each stage.

It is worth noting that this model emphasizes the alignment and interaction of semantic levels. For an innovation to be successfully disseminated, its semantic content needs to maintain a certain degree of consistency and coherence between different subjects. Professor Yucong Duan refers to this as the semantic consistency principle: when an innovation enters a new semantic space, its semantic elements at each level should be mapped as accurately as possible, without distortion or misinterpretation. This involves, for example, the standardization of concepts and terminology, and a unified semantic model for knowledge representation. In the DIKWP×DIKWP model, mapping relationships are established between the corresponding layers of the two semantic systems to ensure semantic equivalence or convertibility. For example, a "knowledge" point from the sender can be recognized by the recipient as equivalent "knowledge" rather than just a meaningless string of data. This semantic alignment is a prerequisite for the smooth extension of the innovation's semantic trajectory. To achieve this, technical and normative support is needed, including ontology term alignment, knowledge graph mapping, semantic interoperability standards, and so on. In fact, semantic standardization is one of the key challenges in building a future semantic-enabled ecosystem: only when semantic resources from different sources follow a unified standard can they be interconnected. The DIKWP model provides a layered semantic definition framework that lays the foundation for standardization, while the DIKWP×DIKWP interaction structure further specifies the way different semantic systems should connect.

In terms of the value mapping path, the DIKWP×DIKWP model reveals how the value of innovation is transmitted and transformed in the semantic conversion across subjects. The value of an innovation is not only reflected in its own semantic content but also depends on how the recipient integrates it into their own system and generates benefits. For example, a new technological invention, as the crystallization of the innovator's wisdom (W layer) and purpose (P layer), when mapped to the semantic space of an industrial partner, may become their knowledge (K layer) input, which is then applied to product improvement to create commercial value. In this process, the mapping of value is: the innovator's wisdom value, after semantic transformation, becomes knowledge or tool value that the recipient can utilize. The layered structure of the DIKWP model enables us to quantify or evaluate the value-added at each layer: from the raw value at the data layer, to the contextual value provided by the information layer, the methodological value embodied in the knowledge layer, the decision-making value brought by the wisdom layer, and finally the goal value achieved at the purpose layer. Research shows that by elevating a large amount of internally generated data to information and knowledge, its value density can be increased; at the same time, by externalizing one's unique wisdom and purpose into services or products, new value can also be created. This means that the value trajectory of innovation is not linear or single-point, but rather accumulates layer by layer in the semantic space and is continuously redistributed in cross-subject interactions. We can understand this as a semantic map of value: it marks the semantic nodes that an innovation passes through from its birth to its application, as well as the value attached to each node and who obtains it. Such a semantic value map helps to identify the key links in value creation and the nodes where value stagnates or is lost. For example, if an innovation generates great value at the knowledge layer but fails to be transmitted to the user's wisdom layer due to semantic incompatibility, then this part of the value is wasted. Through semantic modeling, we can discover and optimize these issues.

In practice, Professor Yucong Duan's team has also proposed the idea of combining DIKWP×DIKWP with blockchain technology to build a "semantic blockchain" for recording and managing this cross-subject flow of semantic value. The so-called DIKWP×DIKWP semantic blockchain introduces the five-layer DIKWP structure onto the blockchain, so that each record on the chain contains semantic metadata such as data, information, knowledge, wisdom, and purpose. In this way, each block can be seen as a DIKWP model, and the blockchain shared by different nodes connects multiple DIKWP models, achieving network-wide alignment at the semantic level. Vertically, the semantic link of a single piece of content from data to purpose is completely traceable; horizontally, the content of different participants at the same level (such as the knowledge layer) can be associated and compared, achieving trusted semantic interaction. This structure is actually a form of implementation of the DIKWP×DIKWP interaction model: one party publishes innovative content as a transaction with a five-layer structure, and the other party reads and parses these layers through the blockchain to achieve semantic understanding and value acceptance. Under the guarantee of the blockchain's trust mechanism, semantic exchange becomes more reliable, and the contributions and benefits of all parties to the innovation are recorded, making the innovation's semantic trajectory traceable and immutable. This lays the technical foundation for the innovation establishment and pricing mechanisms to be discussed later: only when the semantic trajectory of an innovation is clearly visible and the path of value flow is transparent can we better establish who contributed what, what the value is, and conduct fair pricing.

3. Innovation Establishment Mechanism: Identifying Originality, Contribution, and Ownership Based on Semantic Sovereignty and Semantic Consistency Principles

In the digital semantic world, the innovation establishment (rights confirmation) mechanism is crucial. It addresses how to confirm the originality of an innovation, determine the contributions of various parties, and clarify the ownership of intellectual property. Traditional innovation rights confirmation often relies on legal procedures such as patent applications and copyright registration. However, in a globally interconnected semantic environment where AI participates in co-creation, we need new ideas and tools. The concept of semantic sovereignty proposed by Professor Yucong Duan and its extensions provide guiding principles for innovation establishment: semantic sovereignty refers to the right and ability of a nation or entity to have its own language, knowledge, and value systems accurately and completely expressed in the digital space. This concept was originally intended for the national level to ensure that a country's cultural semantics are not distorted by external forces, but its essence is also applicable to innovation rights confirmation at the micro level—that is, the innovator should have the right to autonomously decide and control the semantic content they create, free from infringement or plagiarism by others. This implies two requirements: (1) the source of the innovative semantic content must be traceable to determine who the original creator is; and (2) the semantic content must maintain consistency and integrity in its dissemination and use, to prevent it from being distorted into something inconsistent with the original intent. Based on this, the semantic consistency principle plays an important role in innovation establishment: when an innovation is cited or derived by different platforms and users, we should ensure the consistency of the core semantics (concepts, logic, purpose) to accurately identify the original contribution.

To identify originality, we can use semantic fingerprinting technology and semantic graph comparison methods. The semantic content of each innovation (such as the core argument of a paper, the key idea of an algorithm, the melodic structure of a piece of music, etc.) can be extracted to form a unique semantic feature vector or knowledge tuple, creating a "semantic fingerprint." By comparing the semantic fingerprint of new content with existing knowledge bases, we can determine its novelty and difference from existing achievements. When the semantic similarity is low and no precursor nodes can be found in the knowledge network, it can be preliminarily identified as original. At the same time, using the immutable records of the DIKWP semantic blockchain, we can assign a timestamp and contributor ID to each innovation on the chain, achieving a "first-come, first-served" rights confirmation: whichever entity first records a certain semantic content on the chain, its originality can be established. For example, if researcher A proposes a new theory and stores its key knowledge points in a DIKWP structure on the semantic blockchain, then even if others later propose similar ideas, the on-chain record can prove that A is the original proposer. This is like establishing a semantic property registration office in the digital space.

Furthermore, because each record in the DIKWP blockchain is divided into five levels—data, information, knowledge, wisdom, and purpose—we can identify and quantify contributions at a fine-grained level. A complex innovation is often completed through the contributions of multiple people over multiple stages: some provide the basic data, some refine it into information patterns, some elevate it to theoretical knowledge, some apply it to form wise decisions, and some set the overall goal and direction. Traditionally, it is difficult to objectively measure the contributions of each participant, but the semantic layered model makes this possible. In the semantic blockchain, the contributor can be marked for the content of each layer, and ownership or usage rights can be bound to it. Professor Yucong Duan points out that this mechanism can assign ownership and usage rights to different contributors at various levels. That is, if developer B writes the code implementation based on A's theory (a contribution at the knowledge layer), and company C applies this code to a product to achieve commercial success (contributions at the wisdom and purpose layers), then both B and C should receive corresponding rights and benefits for their contributions at their respective levels. The semantic blockchain can pre-set such equity distribution rules through smart contracts: when an innovative achievement generates revenue, contributors at different levels receive a share of the revenue according to a pre-determined ratio or weight. This actually establishes a peer-to-peer incentive mechanism, ensuring that innovators at each contribution stage are recognized.

In terms of ownership, semantic sovereignty emphasizes that each subject has the right to control and dominate the semantic content they create. This means that without permission, others or institutions may not arbitrarily tamper with or conceal the semantic imprint of the original creator. This is particularly crucial in the context of AI-generated content and collaborative creation. For example, when multiple AIs and humans co-author an article, we need a mechanism to indicate the main contributor of each paragraph (which could be a specific model or a person) to prevent future issues of unclear responsibility or infringement disputes. Similarly, semantic consistency requires that subsequent citations of an innovation maintain a clear attribution to the original creator, without misattribution or obscuring the source. Technically, this can be achieved by embedding semantic watermarks in the content or using white-box testing methods to track the evolution of the content. The DIKWP white-box AI evaluation system developed by Professor Yucong Duan can decompose the AI decision-making process into five semantic layers, allowing for transparent auditing of each layer. This idea can be extended to track and audit the dissemination of innovation: viewing the diffusion process of an innovation as a chain of semantic reasoning and transformation, by monitoring each step, we can find out when and where new contributions were introduced, and when and where it deviated from the original semantics. If a derivative work is highly dependent on the original work in terms of semantics and does not make substantial innovations, the system can identify it as an extension rather than an original work and link it to the original innovation; on the other hand, if enough new semantic elements are derived, it can be confirmed as a new innovation node, but still referencing back to the original node to ensure a clear lineage.

The principles of semantic sovereignty and consistency also need to be implemented at the policy and governance levels to ensure the effective operation of the innovation establishment mechanism. Countries or industries can establish semantic rights confirmation platforms to provide innovation registration and query services. As some scholars have suggested, the possibility of treating data and knowledge as resources for which rights can be confirmed should be explored in intellectual property law, clarifying the ownership and benefit-sharing rules for AI-generated content. For example, it could be stipulated that for content generated by a model trained on a certain public dataset, the original data providers have a certain proportion of derivative rights; or, enterprises and individuals could be encouraged to publish their innovative ideas in an open semantic protocol (such as the DIKWP format) on a rights confirmation platform, and the burden of proof for unregistered content in legal disputes would be higher. These measures should be supplemented with ethical and compliance requirements, such as requiring AI-generated content to automatically label whether it contains components trained from others' works, to protect semantic consistency and the rights of original creators.

In summary, the innovation establishment mechanism in the semantic space needs to achieve the following: confirming originality (who first created the semantic content), quantifying contribution (how much innovative value each participant contributed), clarifying ownership (who owns the intellectual property and benefits), and protecting integrity (not being misinterpreted during dissemination). Through the perspective of semantic sovereignty, we ensure that the original creator has control over their innovative semantics; through the principle of semantic consistency, we ensure that innovation is correctly inherited and developed without losing its source information. This set of mechanisms requires both technical support (such as semantic blockchain, semantic fingerprinting, watermarking, etc.) and institutional guarantees (such as legal provisions for semantic rights confirmation, dispute arbitration mechanisms). When the two are organically combined, we can establish a fair and transparent establishment system for the endless stream of innovative achievements in the digital world, fully incentivizing originality and protecting contributions.

4. Innovation Pricing Mechanism: A Pricing Model Based on Semantic Value Metrics (Semantic Scarcity, Cognitive Alignment, Depth of Induced Chain, etc.)

After determining the ownership and contribution of an innovation, the next important question is how to price the innovation, that is, to measure its economic value and translate it into a price that can be traded or distributed. In a traditional economy, the pricing of goods and services is based on factors such as supply and demand, scarcity, and production costs. In the digital semantic economy, however, innovation often manifests as intangible assets like knowledge, algorithms, and creativity, making their value assessment more complex. Therefore, it is necessary to introduce semantic value metrics and construct a scientific innovation pricing model so that the pricing of an innovation not only reflects its semantic content and influence but is also acceptable in the market.

1.Semantic Scarcity: This is a metric that measures the uniqueness and irreplaceability of an innovation. Similar to supply scarcity in a traditional economy, semantic scarcity refers to how many substitutes with the same semantic content or function exist. If an innovation proposes an unprecedented concept or solution and no similar node can be found in the entire semantic network, then its semantic scarcity is extremely high, indicating its unique value. For example, a brand-new mathematical theory or a groundbreaking technological invention, whose core principles did not have substitutes before being proposed, are of high value due to their unique semantic contribution. Conversely, if an innovation is just a minor modification or combination of existing concepts, its semantic scarcity is low, and its pricing should be relatively conservative. Semantic scarcity can be assessed through knowledge graphs or patent/paper metrics: by counting how many existing nodes have similar semantics to the innovation (by calculating the similarity of semantic fingerprints), or by seeing how many existing technologies can achieve similar purposes. Innovations with high scarcity often enjoy a monopolistic position in the market, and their pricing power is also stronger.

2.Cognitive Alignment (or Cognitive Fit): This refers to the degree to which an innovation matches the existing cognitive frameworks of users or society. An innovation with high cognitive alignment means that users can easily understand and adopt it, and it is highly compatible with existing knowledge systems or users' mental models. An innovation with low cognitive alignment, on the other hand, may be too advanced or heterogeneous, making it difficult for users to accept or requiring a long period of education. This metric has a complex impact on pricing: on the one hand, an innovation with high cognitive alignment is easily and widely adopted, has a large potential market, and should theoretically have a high value; but on the other hand, because it is "easy to understand," it may also be easier for others to imitate and catch up, or there may be many competitors, thus lowering the price. In contrast, an innovation with low cognitive alignment (such as a disruptive theory), although difficult to disseminate in the short term, may have extremely high long-term value because it opens up a completely new paradigm. To consider this factor in pricing, we can introduce a cognitive content premium/discount: if an innovation can seamlessly integrate with existing systems (high alignment), it has good prospects for rapid commercialization, and a premium is given in the valuation; if an innovation needs to cultivate the market (low alignment), the short-term return is uncertain, and a discount is appropriate in the valuation. The quantitative assessment of cognitive alignment can be done through user testing, expert surveys, etc., such as calculating the comprehension accuracy and willingness to accept the innovation concept among target users.

3.Depth of Induced Chain: This metric focuses on how deep and wide the subsequent innovation levels inspired by an innovation are. An important innovation is often not an isolated achievement but opens the door to a series of subsequent innovations. For example, the invention of the laser led to innovative applications in numerous fields such as communication, medicine, and manufacturing; the invention of internet protocols triggered the internet and its trillion-dollar application ecosystem. We can abstract this impact as an innovation chain: how many generations of subsequent innovations (depth) and how many different fields or scenarios of applications (breadth) are triggered by an innovation as the starting point of the chain. An innovation with a high depth of induced chain means it has platform or foundational value and should often be priced higher because its value is not only its own function but also the ecological effect it brings. In the semantic space, we can measure this through the evolution of knowledge graphs: if a new knowledge node is continuously cited and derives many new nodes, and it progresses deeper layer by layer, then its chain depth is high. The chain depth can also be considered in conjunction with the time dimension—some innovations may quickly trigger a large number of follow-up innovations in the short term, but their long-term momentum is insufficient; other innovations may have a lukewarm initial response but become mainstream many years later (their chain is slow but far-reaching). Therefore, the pricing model can be designed to be dynamic, adjusting the valuation by observing the development of the innovation chain over time. For example, the initial price can be set conservatively, but if the citation rate and number of derivative innovations of the innovation are monitored to grow exponentially, the valuation should be promptly increased.

4.Semantic Value Level Index: According to the DIKWP model, the value forms of semantic content at different levels are different. The value of innovation at the data layer may be reflected in originality and completeness; the information layer in timeliness and reliability; the knowledge layer in effectiveness and scope of application; the wisdom layer in the improvement of decision-making quality; and the purpose layer in the degree of achievement of strategic goals. Therefore, sub-metrics can be established for each semantic level of an innovation. For example: semantic completeness (data/information layer, referring to whether the information description of the innovation is comprehensive without major omissions), knowledge effectiveness (knowledge layer, referring to the degree to which the innovation's knowledge effectively solves practical problems), insight depth (wisdom layer, referring to how much more insightful the innovation is than conventional methods), and purpose fit (purpose layer, referring to the degree to which the innovation's outcome aligns with user/social goals). These metrics can be obtained through expert scoring or quantitative evaluation models and weighted into a comprehensive semantic value index. For example, an AI-assisted diagnosis innovation can be evaluated at the data layer for the quality of the dataset used, at the knowledge layer for the height of its medical mechanism innovation, at the wisdom layer for the improvement in diagnostic accuracy, and at the purpose layer for whether it fits the goal of reducing medical costs/improving efficiency. If the comprehensive index is high, it means that the innovation performs well at all semantic levels and should be priced higher.

5.Market Demand and Semantic Substitutability: Although the focus is on semantic measurement, pricing ultimately needs to consider the market supply and demand environment. Semantic substitutability refers to whether other different semantic paths can achieve similar value. If there are completely different solutions that produce the same effect, then even if the innovation is semantically sophisticated, its pricing will be limited. Conversely, if the innovation is tied to a specific semantic system that is difficult for others to bypass (for example, a patented technology becomes part of an industrial standard, and the substitution cost is high), then its value is more stable. Professor Yucong Duan proposes that in the future, semantic resource trading platforms may emerge, where parties can trade resources at all levels, such as data, information, knowledge, wisdom, and even purpose. In such a market, auction mechanisms and dynamic pricing models will play a role: when many buyers compete to purchase a certain innovative knowledge service, the price will naturally rise; conversely, if demand is scarce, it will fall. But unlike traditional markets, the trading objects on this platform are multi-level semantic assets, so the pricing algorithm needs to consider the substitution and complementary relationships across levels. For example, an innovative knowledge product (KaaS, Knowledge-as-a-Service) may partially replace another wisdom service (WaaS), and the algorithm needs to dynamically adjust prices based on user preferences at different levels to achieve market clearing and balance.

In summary, the innovation pricing mechanism can be designed as a multivariate function, with inputs including: semantic scarcity (S), cognitive alignment (C), chain depth (L), level value index (V), and market supply and demand factors (M), i.e.,

Price = f(S, C, L, V, M).

Here, S, C, L, and V come from the analysis and evaluation of the innovation's semantics, while M comes from real-time market data (such as usage rate, bidding situation). This function can be trained on historical cases using machine learning to fit which combinations of semantic features correspond to higher commercial success rates and economic value. It is worth mentioning that this pricing mechanism is not a static label but should be dynamically responsive: after an innovation is applied, its actual performance (such as the social and economic benefits generated, the number of subsequent patent citations, etc.) will further calibrate the above metrics. For example, if an innovation was originally valued moderately, but proves to be extremely important in practical application and triggers a large number of subsequent innovations (the chain depth greatly increases), the model should respond in a timely manner and increase its price evaluation; and vice versa.

Professor Yucong Duan's thoughts on symmetrical economics remind us that pricing is not only for market transactions but also for incentivizing a peer-to-peer exchange of value creation. Therefore, the innovation pricing mechanism should ensure that contributors receive returns commensurate with their semantic contributions, while the price paid by users matches the value they receive (this will be discussed in detail in the next section). Semantic metrics provide a ruler to make this "equivalence" justifiable. For example, through blockchain smart contracts, the price paid by the buyer can be automatically distributed to contributors at different levels according to pre-set semantic value weights. This is not only transparent and fair but also encourages future innovators to pay more attention to enhancing the semantic value of their ideas—because they know that the market will pay for truly meaningful innovations.

5. Symmetrical Economics Perspective: How Innovation Forms a Peer-to-Peer Value Exchange Among Interacting Parties and Maintains Fair Incentives Through a Symmetrical Structure

The symmetrical economics perspective focuses on how, in the process of innovation generation, dissemination, and exchange, all participating parties can achieve a peer-to-peer exchange of value, avoiding a situation where one party profits excessively while the interests of another are harmed, thereby maintaining fair incentives and the sustainable development of the entire innovation ecosystem. "Symmetry" here refers, on the one hand, to informational and semantic symmetry—all parties have a transparent and nearly balanced cognitive basis, with no serious information asymmetry; and on the other hand, to symmetry in value exchange—what is given and what is received are relatively equivalent, with no exploitative, unfair distribution. In traditional economics, asymmetrical information often leads to market failure or imbalanced interests. In the semantic-enabled digital world, however, new technologies and mechanisms offer the potential to alleviate cognitive asymmetry and achieve a symmetrical pattern of co-creation and co-sharing of value.

Although Professor Yucong Duan's theory does not directly use the term "symmetrical economics," many of his designs in semantic blockchain and the DIKWP model aim to build an innovation ecosystem of multi-party collaboration, mutual trust, and win-win, which embodies the spirit of symmetrical economics. For example, the aforementioned DIKWP semantic blockchain allows each participating party to join the network as a node, with no centralized controller, and all nodes are equal in their contribution and acquisition of semantic content. The consensus mechanism and immutability of the blockchain ensure trust symmetry: everyone has a common trust in the on-chain records and jointly supervises them. This design, which integrates the trust dimension with the semantic dimension, establishes a peer-to-peer, trustworthy structure for innovation value exchange. All parties publish their own semantic assets on the chain while also consuming the semantic assets of others, and every transaction is open and transparent, making it difficult for any party to deceive others using information advantages. This fundamentally creates a symmetrical trading environment.

In this environment, how does innovation form a peer-to-peer value exchange? This can be illustrated with a sample scenario: on an AI collaborative creation platform, let's assume there are four parties involved in the innovation chain: developers, content creators, AI model providers, and users. The developer writes the algorithm (knowledge contribution), the content creator provides creative materials (data/information contribution), the AI model provider offers a well-trained large model (wisdom tool contribution), and the user proposes the need and ultimately benefits (purpose-driven). According to the idea of symmetrical economics, the inputs of all parties should be rewarded, and the outputs all come from the inputs of others, forming a cyclical, symbiotic relationship. Using a semantic blockchain, this platform records the contributions of all parties: how many times whose algorithm was used, whose materials were mixed into how many works, how much creation the model provider's service supported, how the user's feedback data fed back to optimize the algorithm, and so on. Then, through smart contracts, the value is automatically settled: the revenue from the sale of the work is distributed among the four parties according to the proportion of their contributions, and each party receives exactly their share of the overall value-added. From an economic perspective, this achieves marginal contribution returns belonging to each party, with no single party unilaterally seizing excess profits. For example, traditional content platforms often have the platform taking an excessive cut and the creators receiving a low share, which is an asymmetrical incentive that will damage creative enthusiasm in the long run. Under the new peer-to-peer architecture, the platform is just one node in the network, and the services it provides (such as computing power, algorithms) receive remuneration commensurate with its contribution, and it no longer holds a dominant position. This multilateral value co-creation and sharing model embodies symmetrical value exchange.

Let's take industry-academia-research co-creation as another example. On an open research platform, a university, a company, and an independent researcher collaborate to complete an innovative invention. The university provides the basic theory (K-layer knowledge), the company provides experimental data and application scenarios (D/I layers), and the independent researcher contributes unique insights (W layer). The final result, such as a patent or a paper, generates economic and reputational benefits. Traditionally, the company that funds the project often holds the majority of the rights, while the scholars receive only a small remuneration or authorship, which can dampen the enthusiasm of the academic community to participate. But under the concept of a symmetrical economy, all parties share the value according to their actual semantic contributions, which is more equitable. If the company's data is a key part of the innovation, it will receive a corresponding share of the profits based on its weight; but if the scholar's theoretical innovation is the core, then their share of the profits should match that. By recording every data share, theory submission, solution decision, as well as subsequent patent citations, product sales, and other events on a semantic blockchain, we can design a distribution formula based on a contribution algorithm to achieve peer-to-peer distribution according to contribution. This ensures that every unit of innovative content (regardless of its source) receives every unit of return, no one's knowledge is expropriated without compensation, and no one gets something for nothing.

The symmetrical structure is also reflected technically in the flattening of the network structure and the communization of governance. Decentralized platforms such as blockchain networks and ontology alliances allow all participating nodes to jointly maintain the system, and this structure itself is symmetrical. In addition, the introduction of open standards and open interfaces is also a means to achieve symmetry: for example, when a large enterprise opens up its knowledge graph and model interfaces to small and medium-sized enterprises, it essentially makes originally monopolized data/knowledge resources symmetrically accessible. Although the large enterprise contributes its resources, it can also benefit from the innovative applications of small and medium-sized enterprises (for example, by discovering new business opportunities and improving the ecosystem), forming a virtuous cycle. As policy recommendations advocate, an industrial atmosphere of "a leading goose flying, a flock of geese following" should be cultivated, encouraging leading enterprises to drive the collective innovation of the group. This statement vividly illustrates the pursuit of a symmetrical economy: to leverage the strengths of the leaders while ensuring that followers have opportunities to participate and benefit, so that the entire team advances together, rather than being polarized.

The maintenance of fair incentives also requires institutional guarantees. The ideal of symmetrical economics is that everyone receives their due returns based on their contributions, but in reality, it is necessary to prevent certain subjects from exploiting loopholes in the rules for profit or the emergence of new asymmetries. To this end, governance strategies can introduce third-party evaluation and credit mechanisms. For example, a semantic sovereignty compliance assessment could be established to evaluate whether a company's data products and AI systems are biased or violate fairness; for innovation trading platforms, a credit scoring mechanism could be introduced, so that if a party is repeatedly found to not share profits according to the agreement or to steal the achievements of others, its credit is lowered and its transactions are restricted. Through these measures, asymmetrical behavior can be suppressed, and combined with laws and regulations that clarify the rights and obligations of all parties involved in innovation (such as specifying rules for knowledge sharing and benefit distribution in the law), an overall symmetrical and fair innovation economic order can be maintained.

It is worth mentioning that symmetry does not mean rigid egalitarianism. It emphasizes equality of opportunity and distribution according to contribution, not that everyone's absolute returns are the same. In real innovation activities, it is normal for people with different talents and resources to have different returns, but the difference must come from the difference in the value they actually create, not from information monopoly or authoritarian distribution. What symmetrical economics aims to do is to measure everyone's value contribution clearly through semantic transparency and mechanism design, and to distribute accordingly, while reducing market failures caused by a lack of information transparency. In the long run, such a system will greatly stimulate innovation enthusiasm: because people believe that the more you work, the more you get, and the greater the contribution, the higher the return. At the same time, because resource acquisition is also more symmetrical and open, small and medium-sized innovators will not be squeezed out, and they can more easily obtain the elements of innovation (data, knowledge, tools, etc.) to invest in innovation, thereby lowering the barrier to innovation. This is fundamentally different from the traditional monopoly oligopoly pattern, where innovation resources are concentrated and the distribution of benefits is unfair, which often dampens the innovation motivation of latecomers.

To summarize with a vivid metaphor: a symmetrical semantic economy is like an ecosystem, where different species have their own roles but are interdependent and prosper together; it is not a state of imbalance where those at the top of the food chain seize most of the energy while those at the bottom struggle to survive. In the future semantic-enabled digital world, by adopting a symmetrical economics perspective in designing the innovation system, we can expect to see the light of innovation value shining on every participant, thereby building a sustainable open innovation ecosystem.

6. Application Scenario Analysis: AI Collaborative Creation, Open Education Platforms, Collaborative Research Mechanisms, Platform Economy Content Revenue Sharing, etc.

To make the above theories more concrete, this section selects several typical application scenarios for analysis, exploring how to practice the semantic establishment and pricing of innovation in these scenarios, and the corresponding mechanism design.

(1) AI Collaborative Creation System: With the development of generative artificial intelligence (AIGC), human-machine collaborative creation has become a reality. On a platform where AI and humans collaborate on creation, such as jointly creating music, developing code, or writing news, innovation is often completed by multiple subjects. In the traditional model, without detailed mechanisms, it is difficult to distinguish how much creativity AI contributed as a tool and how many ideas humans provided, leading to ambiguous ownership of rights. By adopting semantic tagging and blockchain tracking, we can introduce a new paradigm for innovation establishment and pricing in collaborative creation.

In terms of specific implementation, the collaborative platform can perform semantic modeling of the creation process based on the DIKWP model: viewing the creation process as a continuous cycle of data -> information -> knowledge -> wisdom -> purpose. For example, in text writing, AI generates a first draft (information layer content) based on the theme provided by a human (purpose), the human polishes it and adds factual information (knowledge layer), AI summarizes the article structure to improve readability (wisdom layer), and finally, a finished product that meets the reader's needs is formed (achieving the purpose). Throughout the process, every piece of new content or modification can be given a semantic tag, indicating who (a person or an AI model) added or changed it, under what context, and to which semantic level of the creation it belongs (e.g., adding a fact = knowledge layer contribution, improving wording = information layer contribution). These operation records are written into a consortium blockchain, shared by all participating nodes (including the AI model owner). Once the work is completed and enters the market for sale or publication, the on-chain records can be used for revenue sharing: the contribution ratio of each party is measured based on the semantic level importance and quantity of the contributed content, and the revenue is automatically allocated through smart contracts. For example, if a financial report is initially drafted by AI, a human journalist adds key investigative data and adjusts the wording, and finally, an editing AI optimizes the headline for attractiveness—then the human journalist contributes accuracy and exclusive information (high-value knowledge layer contribution), the drafting AI provides the initial structure (a large amount of text at the information layer, but relatively low-value, repetitive contribution), and the editing AI contributes wisdom that enhances communication effectiveness (wisdom layer contribution). Based on this, a distribution plan can be set, such as the journalist receiving 50% of the revenue, the drafting AI provider 30%, and the editing AI provider 20%, with the specific proportions determined by the quantified results of the contributions. Such a collaborative system ensures that each party's investment receives a commensurate return, incentivizing both humans and AI to leverage their respective strengths and jointly improve the quality of creation. At the same time, semantic tagging can also achieve version traceability and responsibility attribution: if false information appears in the work, it can be traced back to whether it was in the AI-generated part or the data provided by a human, providing a basis for content review and copyright disputes.

In terms of pricing, the content produced through this collaboration can also be priced based on semantic value metrics. For example, the price of a song co-created by an AI and a musician depends not only on market conditions but also on its semantic scarcity (the degree of innovation in style or melody), cognitive alignment (whether it conforms to popular aesthetics), and chain depth (whether it has started a new trend in music style). The platform can dynamically adjust the price and sharing ratio based on user feedback (play count, ratings), forming a self-optimizing incentive loop.

(2) Open Education Platform: In the field of education, the semantic-enabled world has brought a new model of open education—where teachers, students, and AI all participate in the creation and dissemination of knowledge. Imagine a global open education platform where any teacher can upload course materials (videos, courseware), students can contribute solutions to exercises or study notes, and AI can generate quizzes based on the content and provide personalized tutoring. The innovation on such a platform is reflected in the continuous improvement and localization of teaching methods and textbook content, as well as the sharing and reuse of learning resources.

Applying semantic innovation theory, this platform should establish a semantic archive and a contribution list for every piece of content. For example, the knowledge graph of an open course could map out: the original course outline was written by a certain professor (knowledge layer, contributor A), which cited certain public case data (data layer, contributor B), and later another teacher optimized the teaching logic of the video lectures (wisdom layer, contributor C), and an AI system adjusted the difficulty of the practice questions based on student feedback (wisdom layer, contributor D)... Through this semantic network representation, the innovation trajectory of the course and the contribution of each person are clear at a glance. The innovation establishment mechanism here is embodied in the confirmation of intellectual property in teaching and contribution. For example, Professor A has the main semantic sovereignty over the core structure of the course, B's provided data cases, if exclusive, also enjoy data layer property rights, and the improvements made by C and D are secondary innovations but should also be recognized. The platform can set a knowledge contribution index for each course and distribute the revenue generated by the course (e.g., student fees, platform subsidies) to the contributors according to the index. Even in a non-profit environment, this confirmation is meaningful—those with high contribution levels receive corresponding honorary value or incentives in academic evaluation and reputation, such as being awarded an open education contribution award.

For student users, the symmetrical economy significance of this platform is that students are no longer just passive consumers but can also become knowledge contributors and benefit from it. For example, if a student organizes a set of course notes with their own extended thoughts, which are adopted by many subsequent learners, the platform can give this student a certain reward (such as a scholarship or tokens), because their notes have improved the overall learning experience—equivalent to providing a value-added service at the information layer. Similarly, high-quality questions and error corrections proposed by students should be seen as improvements to the educational resources and have corresponding value. Through such a mechanism, the open education platform fosters a culture where everyone can contribute and is incentivized according to their contribution, promoting the continuous iterative innovation of teaching content. AI, as a tool provider, is not a free laborer either: if an AI model is specifically used on this platform to answer questions or grade assignments, the model owner can receive revenue based on the frequency and effectiveness of the model's service. This is similar to treating the wisdom service of an AI teacher as a billable innovative input, providing a direct return for developing high-quality educational AI (incentivizing the integration of the AI industry and education).

(3) Collaborative Research Mechanism: Modern scientific research collaboration is increasingly interdisciplinary and inter-institutional, and collaborative research needs to break down information and interest barriers. By applying the ideas of semantic sovereignty and innovation pricing, new models for scientific research collaboration can be designed. For example, a "scientific research semantic collaboration platform" could be established, where researchers share experimental data, algorithmic models, intermediate theoretical insights, etc., and each contribution is registered. When the final results, such as papers, patents, or products, are formed, all contributors share the benefits of the achievements according to their contributions. This is somewhat similar to "open-source research" or "crowdsourced research," but with a more refined quantification of each step's contribution.

For example, in a pharmaceutical research project, a university laboratory uploads a batch of biological experiment data, a biotech company provides data analysis tools, an independent scientist proposes a key hypothesis (theoretical knowledge), and finally, a pharmaceutical company develops a new drug and profits from it by integrating the above inputs. In the traditional model, the company might end up with the lion's share of the benefits, while other contributors either sign a contract for a fixed fee or are just listed as authors on a paper, with no direct financial gain. But through a semantic co-creation platform, data rights confirmation and trusted circulation can be achieved: all experimental data is traceable and its ownership is clear through the platform, and its use is based on agreed-upon payment or equity exchange (for example, the laboratory contributes data as an equity stake in the project); the contributor of the theoretical hypothesis, because they provided a knowledge breakthrough, can gain inventor status and a share of the dividends in the project's patents, and can even be rewarded immediately in the form of tokens or research credits. When the new drug is successful, the profits will be automatically distributed to the various contributing parties through smart contracts according to a pre-determined ratio—these ratios are agreed upon at the start of the project, for example, based on the value of the data and knowledge. In this way, the investors at each research stage can receive a commensurate return, forming a mechanism for sharing research achievements, rather than a single institution monopolizing the results. It has been mentioned that some organizations have already tried to create digital knowledge profiles for their employees, semantically linking their knowledge and experience at work to form an organizational knowledge network and confirming rights for its accumulation. This is actually similar to a research team: each researcher provides data and knowledge, and their knowledge is confirmed in the model for accumulation, which is convenient for settling contributions later. Through semantic indexing, a research team can clearly see who proposed which idea and whose data was used, thus reasonably determining the author order or patent inventor order when publishing papers and applying for patents. This avoids human bias and disputes, making incentives fairer and collaboration smoother.

In addition, a collaborative research platform can also introduce the concept of innovation pricing to guide resource allocation. For example, under a popular research topic, different solutions (different theoretical paths) can recruit collaborators on the platform. An initial valuation can be given to each solution based on its semantic novelty and potential chain depth, and funding agencies (government, foundations) can decide which solution to invest in or how to allocate funds based on this. This is similar to a "crowdfunding + bidding" model for research projects: novel and promising ideas have a high valuation and are more likely to raise more resources; mediocre, similar ideas have a low valuation and need to be improved before they can get support. This encourages researchers to be bold in proposing innovative ideas with high semantic value, and systemically avoids the investment of resources in follow-up research, truly supporting originality.

(4) Platform Economy Content Contribution Revenue Sharing: In digital content platforms (such as video platforms, knowledge Q&A communities, social media), users are both consumers and producers (UGC). How to fairly share the platform's revenue among content contributors has always been a topic of discussion in the industry. The traditional traffic-based revenue sharing model often distributes revenue based on clicks and views, but this may encourage low-quality clickbait rather than high-quality content. After introducing semantic value metrics, the platform can share revenue based on the semantic influence of the content. For example, in a knowledge Q&A community, an answer is evaluated not only by its view count but also by its knowledge value (whether it solved the questioner's problem, whether it sparked many discussions/citations). Using the depth of induced chain metric: if an answer leads to many subsequent answer citations, is included in a wiki or blog for secondary creation, its chain depth is high, and it should receive a higher weight in the revenue distribution. Or, on a video platform, if an original short video idea is widely imitated and passed on (broad chain depth), the original author should receive a portion of the revenue from this derivative traffic—this can be achieved through the platform's semantic detection (identifying video memes). For example, with the popular challenge ideas on TikTok, the original creator is often overlooked; symmetrical incentives require the platform to return a certain proportion of the subsequent billions of views' revenue to the original idea provider.

In the platform economy, there is also an asymmetry between "big V"s (verified, popular accounts) and "small V"s. Big Vs control the traffic, making it difficult for small content creators to stand out. The semantic-enabled world can help long-tail content find its audience through semantic association, thereby mitigating the Matthew effect. For example, through semantic tags and personalized recommendations, valuable niche content can be matched with users who need it, instead of being squeezed out by top content. This makes the content ecosystem more prosperous. At the same time, in revenue sharing, a multi-level model can be adopted: part of the platform's revenue is shared based on traffic, and part is shared based on a content value score. The content value score can be generated from a combination of user feedback and expert reviews, including evaluations of the content's reliability, originality, depth, etc. These evaluation metrics actually correspond to semantic levels (reliability - knowledge layer, originality - semantic scarcity, depth - wisdom layer). Professor Yucong Duan's discussion on the commercialization of knowledge and wisdom shows that many leading companies are beginning to build multi-level value systems, selling knowledge and wisdom as products. Analogously, in the platform economy, content can be divided into levels—basic entertainment content (information layer, relatively low value but large in quantity), knowledge-based content (knowledge layer, higher value density), decision-making content (wisdom layer, such as professional consulting), and purpose-oriented content (purpose layer, such as customized solutions). The platform can adopt different revenue sharing ratios or incentive tilts for content at different levels to ensure that creators of high-level content receive high-value returns, avoiding a one-size-fits-all approach that leads to bad money driving out good.

What is common to all these scenarios is the flexible application of semantic pricing and rights confirmation of innovation in practice: whether it's AI-human co-creation, educational sharing, collaborative research, or content platforms, they all essentially involve multiple people and multiple AIs jointly contributing semantic content, and then co-creating and co-sharing value. Through means such as semantic tagging, blockchain recording, and smart contracts, we see a trend: every meaningful contribution of every person will be seen, recorded, and reflected in the distribution of value. This will greatly enhance the enthusiasm and sense of fairness of participants in various fields, thus forming an open innovation ecosystem. More importantly, the successful practice of these scenarios will promote each other: for example, the spirit of collaboration according to contribution cultivated in open education will be transmitted to research and industrial platforms; the revenue sharing model of AI collaborative creation can provide a paradigm for human-machine collaboration in other industries. The whole of society will gradually adapt to and embrace this symmetrical and semantically transparent innovation model.

7. Mechanism and Governance Design: Innovation Semantic Indexing Specifications, Pricing Algorithm Framework, Value Settlement Mechanism, and Ethical and Compliance Governance Strategies

To realize the above theories in practice, a supporting mechanism and governance system must be built. Here we will expand on four aspects: semantic indexing specifications, pricing algorithm framework, value settlement mechanism, and ethical and compliance governance strategies.

(1) Innovation Semantic Indexing Specifications: This is a foundational project aimed at establishing a unified standard for describing the semantic content and contribution information of innovations. Semantic indexing specifications include ontology definitions, metadata architecture, and tagging systems. For example, a common set of ontologies needs to be defined to describe the elements of innovation: what is a "data contribution," "knowledge contribution," "wisdom contribution," what is an "original concept," "derivative concept," and so on. Professor Yucong Duan's DIKWP model itself can serve as a top-level ontology framework, mapping the elements involved in innovation activities to the five types of semantic units: data, information, knowledge, wisdom, and purpose. On this basis, for innovations in different application fields (such as healthcare, education, industrial design, etc.), it is also necessary to expand the domain ontologies and semantic tags. For example, innovations in the medical field can be tagged with the semantics of the diseases involved, and in the education field, the difficulty of knowledge points and the applicable age can be tagged.

To ensure smooth semantic docking between different systems, standardization bodies should take the lead in formulating national and industry standards. As policy recommendations suggest, the development of standards covering data, knowledge, semantic blockchain, AI evaluation, etc., should be accelerated. These standards should include: the semantic description format for innovative content (such as a description framework based on JSON-LD or RDF), contributor identity identification specifications, timestamp and version control specifications, and semantic watermark embedding specifications, etc. With standards, different platforms can exchange semantic data on innovation and build a national or even global semantic innovation network. It is particularly worth emphasizing the semantic consistency standard: how to maintain the original semantics of content without distortion when it flows from one system to another. We can draw on the experience of the semantic web and knowledge graphs internationally, and formulate semantic mapping and transformation rules, including synonym replacement, hyponym expansion, ambiguity resolution, and other methods.

(2) Pricing Algorithm Framework: Based on the value metrics discussed in Section 4, specific algorithms and models need to be designed to achieve automated innovation pricing and value assessment. This may involve the intersection of artificial intelligence and economics. A two-stage framework can be envisioned: in the first stage, natural language processing and knowledge graph technology are used for semantic analysis of the innovation to extract pricing-related feature metrics (semantic scarcity, cognitive alignment, chain depth, level index, etc.); in the second stage, these features are input into a pricing model, such as a machine learning regression model or a game theory model, to output a benchmark price or a price distribution range.

In the first stage, the semantic analysis module must be able to extract quantifiable metrics from various types of innovations. For example, for an academic paper innovation, the analysis module calculates its semantic similarity distribution with existing literature (scarcity), the possibility of interdisciplinary citation (chain breadth), theoretical complexity (the opposite of cognitive alignment), etc. For a product innovation, it analyzes relevant patents, technical documents, and market demand data to obtain the metrics. Knowledge graphs and semantic mathematics are very useful here: the semantic mathematics framework proposed by Professor Yucong Duan can help define relationships between innovations such as semantic equivalence, inclusion, and derivation. With the help of these mathematical relationships, we can judge whether an innovation is truly new (not derived from other knowledge), or whether it can derive a lot of new knowledge. The results of such semantic reasoning can be directly used as one of the pricing features.

In the second stage, the pricing model needs to incorporate economic factors. The ideas of auction theory or option pricing can be adopted, because innovations often have uncertainty and potential option value. For example, if an innovation is viewed as an asset whose future returns depend on uncertain factors such as market acceptance, then real options models or risk-adjusted discounting methods can be used. A two-sided market game model can also be used: on one side are the innovation suppliers, and on the other are the demanders, with bidding on a platform in between. The algorithm should find a balance between the provider's expectations and the buyer's willingness, while also conforming to the value metrics. Since the value of innovation sometimes has externalities (such as the spillover effects of basic research), the government and platforms may need to intervene in pricing to reflect social value.

A feasible framework is: use machine learning to establish an innovation value scoring model that maps semantic metrics to a value score, and then use an economic model to convert the score into a price. When training this model, historical innovation case data can be used as the training set. For example, past technological inventions can be labeled with the economic benefits they later generated, the number of patent citations, etc., and the semantic features can be used as input for training. The parameters learned by the model will then reflect the marginal contribution of each semantic feature to the value, which can be used as a reference for pricing new innovations in the future. Of course, this requires a large amount of high-quality data and continuous calibration. In addition, different fields may require different sub-models, because, for example, the value realization models of pharmaceutical innovations and software innovations are very different.

(3) Value Settlement Mechanism: This is the execution level of innovation rights confirmation and pricing, involving the specific implementation of transactions and distribution. Value settlement needs to solve two problems: one is transaction settlement, that is, how innovative achievements are traded and monetized; the other is revenue distribution settlement, that is, how the monetized revenue is divided among the contributors.

For transaction settlement, it is recommended to establish a semantic asset exchange or platform. This is similar to the concept of a data exchange but extended to semantic assets at all levels. The holders of innovations can list their innovative content (algorithms, design proposals, patent licenses, creative works, etc.) for sale, specifying the price (which can refer to the automatically generated benchmark price) and transaction conditions (usage license, number of times, duration, etc.). Demanders can browse and inquire, and use smart contracts to complete transactions. Due to the special nature of semantic assets, the exchange needs to support different types of transaction models: one-time sale, per-use/period license, revenue sharing, auction, etc. For example, an invention patent can be auctioned for an exclusive license, or royalties can be charged based on product sales; a piece of music can be shared based on the number of plays, etc. Smart contracts can write these terms in, making the execution automated. The blockchain provides transparency and trustworthiness here, ensuring that transaction records are verifiable, tamper-proof, and at the same time protecting the interests of both parties (for example, the contract can have an escrow clause, ensuring payment is made only when conditions are met).

For revenue distribution settlement, a contribution settlement system is needed. Based on the contribution records and weights formed in the innovation rights confirmation stage, the settlement logic can be triggered every time a transaction occurs or revenue is generated. Each record on the semantic blockchain already indicates the contributors at each level and their equity shares. When the exchange receives payment from the buyer, the on-chain smart contract will automatically transfer the funds to the accounts bound to each contributor according to the proportions. This automatic revenue sharing mechanism has already been practiced in some blockchain applications (for example, after an NFT work is sold, a share is given to the original author as agreed). On an innovation platform, this will generally become the default rule. It is worth noting that to reduce the performance pressure brought by frequent on-chain transfers, small-amount revenues can be designed for cumulative settlement or batch settlement, such as summarizing the revenues of various innovations in the current period on a monthly basis, and then settling and distributing them. This is similar to the periodic dividend distribution model of issuing dividends or royalties.

The value settlement mechanism also needs to deal with cross-platform and cross-border situations. Innovation collaboration is often globalized, for example, the formulation of a standard involves contributions from companies in multiple countries. If each has a different blockchain or platform, how is the value settled in a cross-chain environment? This requires the establishment of a cross-chain mutual recognition protocol or a consortium chain. For example, by using a certain bridging technology, the rights confirmation and revenue on one chain can be mapped to another chain, thereby realizing the circulation and settlement of innovation value on a global scale. At the same time, the issue of currency units needs to be considered—it could be fiat currency, platform tokens, or stablecoins. If tokens are used for settlement, global universality and immediate transfer can be ensured, but the risk of currency value fluctuations also needs to be guarded against. This belongs to the financial layer design, and a balance needs to be found between incentive efficiency and stability.

(4) Ethical and Compliance Governance Strategies: Technology and mechanisms are certainly important, but ethics and regulations are the last line of defense to ensure the healthy operation of the system. First are the ethical principles. The innovation ecosystem of the semantic-enabled network should follow several principles: respect for originality (no plagiarism, no misappropriation of others' semantic achievements, or commercial use without permission), fairness and justice (distribution according to contribution, opposing differential treatment), transparency and openness (important decision-making rules are transparent and auditable), and privacy and security (protecting the privacy of contributors and the security of sensitive data). Platform operators and participants should all sign to abide by these ethical guidelines. For example, the user agreement of a collaborative platform should clearly state: if the submitted content cites others, the source must be indicated, otherwise it will be treated as plagiarism; any attempt to tamper with blockchain records or cheat to obtain benefits will be severely punished upon discovery.

Second is the governance of bias and values. On a globalized semantic platform, different cultural backgrounds have different judgments on value. Semantic sovereignty requires maintaining cultural diversity and information fairness. Therefore, when evaluating the value of innovation, it is necessary to prevent bias in algorithms or manual reviews. For example, an AI evaluation model may underestimate the value of Eastern semantic innovations due to its training data being biased towards Europe and America. This requires bias correction in the algorithm and the addition of multicultural testing. In content review, for innovations that may cause controversy (such as gene editing technology, consciousness uploading, etc.), an ethics committee should be involved to evaluate their social impact and ensure the direction is right.

Third, laws and regulations need to keep up with the development of the semantic economy. It is necessary to study and gradually improve rules such as: the protection of AI-generated content and semantic assets in the "Copyright Law" and "Patent Law," the legal nature of data rights confirmation, and the legal effect of smart contract execution. At present, some countries have begun to explore: for example, legislating to explicitly define data as a type of asset, or making interpretations on the copyright ownership of algorithm outputs. Professor Yucong Duan and others advocate for adding clauses to relevant laws to protect the accurate expression of language, characters, and cultural content, and to prevent semantic bias as a type of security risk. This helps to emphasize the importance of the authenticity and non-distortion of semantic content from a legal height, and also provides a basis for punishing acts of malicious distortion of others' innovative achievements. At the same time, the law needs to clarify the default rules for innovation revenue distribution and dispute resolution mechanisms. For example, it can be stipulated that for multi-party innovation cooperation projects without special agreements, the revenue is shared according to contribution (the DIKWP model can be cited as a legal reference model), to prevent a situation of having no rules to follow when disputes over benefit distribution arise. For the deliberate misappropriation of others' semantic achievements (such as a company hiring researchers to obtain innovations and then not giving them their due rights), the law should also have corresponding punishment and compensation provisions.

Fourth, supervision and third-party governance. The government and industry associations can play an important role in the innovation ecosystem. On the one hand, establish professional evaluation agencies to conduct third-party certification of the semantic value and security of innovations. For example, independent agencies can be established to evaluate the bias of AI systems, the security of semantic blockchain platforms, or review the fairness of large-scale innovation transactions, and issue reports. These certifications can be used as conditions for market access or as a reference for government procurement, which is conducive to survival of the fittest and forces the industry to follow the requirements of semantic sovereignty and the principle of fairness. On the other hand, the government should conduct anti-monopoly and open supervision of platforms to prevent new centralized giants from practicing asymmetry under the guise of symmetrical mechanisms. For example, requiring large semantic platforms to provide data interfaces to the outside world and not to block user assets without reason, and so on. At the international level, promoting the formation of global principles of semantic fairness is also part of the governance strategy. Through multilateral cooperation, it is advocated that all countries jointly oppose semantic hegemony and support open innovation.

In general, the design of mechanisms and governance needs a combination of "hard" and "soft": hard mechanisms provide technical guarantees, and soft governance provides value norms. Only when both are strong can the semantic rights confirmation and pricing system for innovation operate stably and gain widespread trust. By adopting semantic indexing specifications and pricing algorithms, we build a sophisticated machine; through the value settlement mechanism, we give the machine the power to operate; by injecting ethics and law, we install a safety valve and a steering wheel for the machine. In the future, this system may be continuously improved. For example, new institutions such as a "semantic innovation bank" may be born, specializing in innovation value assessment, investment, and revenue distribution services; or a "semantic arbitration court" may appear to quickly resolve semantic property disputes. No matter how the form evolves, its purpose is to ensure that innovators can create with peace of mind, collaborators can share fairly, and consumers can adopt with confidence, thus forming a healthy innovation governance ecosystem.

8. Policy Recommendations: Strategic Suggestions on Semantic Rights Confirmation, Incentive Structures, Open Innovation Ecosystems, and International Semantic Docking Mechanisms

To realize the future vision described above, policy guidance and support from the government are essential. Based on Professor Yucong Duan's theories and practical experience, we hereby propose several strategic policy recommendations, focusing on semantic rights confirmation, incentive structures, open innovation ecosystems, and international semantic docking, to provide a reference for decision-makers.

(1) Incorporate Semantic Sovereignty and Innovation Rights Confirmation into Top-Level Planning: The government should clearly state the importance of semantic rights confirmation in national digital transformation and science and technology innovation strategies. It is recommended to add a special section on "Semantic Sovereignty and Semantic Innovation" to the "14th Five-Year" digital economy plan and related medium- and long-term science and technology plans. Specific measures include: issuing guiding documents to clarify the principles and methods of rights confirmation and innovation value measurement at the semantic level; elevating the construction of a sovereign AI semantic system to a national requirement; and setting phased goals for 2025 and 2030 (such as establishing several semantic blockchain demonstration applications, and initially forming a semantic standard system). The significance of top-level design is to clarify the direction for all departments and regions, ensuring a unified national effort. This can avoid the problem of each going their own way or having inconsistent standards.

(2) Establish a Cross-Departmental Semantic Innovation Coordination Mechanism: Given that semantic rights confirmation and innovation incentives involve multiple fields such as science and technology, industry and information technology, cyberspace administration, education, culture, and intellectual property, it is recommended that the State Council or a central-level body establish a "Semantic Sovereignty and Innovation" coordination working group. This group should bring together the above-mentioned departments as well as the standards committee and expert scholars. Its main responsibilities would be: coordinating policy formulation, promoting the development of semantic technology standards, organizing pilot demonstration projects, and coordinating the investment of resources from various departments. For example, the science and technology department would be responsible for key technological breakthroughs, the industry and information technology department for industrial promotion, the intellectual property office for the design of the legal framework, and the education department for promoting talent cultivation. Through regular meetings and joint projects, this mechanism can break down departmental barriers, form a policy synergy, and accelerate the construction of a semantic innovation ecosystem.

(3) Increase Scientific Research and Talent Cultivation: The state should establish a "Key Technologies for Semantic Sovereignty" special research program. This would fund universities, research institutes, and enterprises to overcome a series of bottleneck technologies, including semantic blockchain, efficient semantic reasoning engines, DIKWP evaluation tools, and cross-lingual knowledge fusion. These technologies are precisely the foundation for realizing semantic innovation rights confirmation and docking. Taking semantic blockchain as an example, it is necessary to solve the problem of efficient storage and retrieval of semantic data on the chain; the semantic reasoning engine needs to quickly infer the associated impact of innovations on a multi-layer semantic network; and cross-lingual knowledge fusion involves international semantic docking (discussed later). At the same time, talent cultivation is also a long-term plan: education departments and universities should establish interdisciplinary subjects or directions in "Semantic Technology and Sovereign AI" to cultivate composite talents who understand both artificial intelligence and are proficient in linguistics and semantics. The establishment of special semantic innovation research centers or courses in colleges of computer science, library and information science, and management can be considered. The government can provide scholarships and research funds to encourage young scholars to enter this emerging field. Regular semantic innovation competitions, hackathons, etc., can also be held to discover excellent projects from the public and include them in the national talent plan for support.

(4) Carry out Pilot Demonstration Projects: Take the lead in implementing semantic innovation mechanisms in some areas and industries with mature conditions to gain experience before promoting them. Specific recommendations: select pilot zones for the digital economy (such as the Hainan Free Trade Port, Xiong'an New Area) to establish experiments in semantic data exchange and supervision. In these experimental zones, a data element market based on the DIKWP architecture would be built, with the government and enterprises opening up data for exchange according to a unified semantic standard, and using semantic blockchain technology to verify the effects of data rights confirmation, circulation, and pricing. Or, in provinces and cities with developed industrial manufacturing (such as Zhejiang, Guangdong), pilot semantic sovereignty industrial internet platforms could be established, where the data and knowledge of all parties in the supply chain are shared on the chain to test the improvement in production efficiency and fair distribution. In pilot areas for smart cities, the semantic sovereignty framework could be embedded in the city's data brain, allowing citizens to participate in semantic supervision. For example, the public could review the semantic explanations of AI decisions, thereby verifying the role of public participation in improving the fairness of decision-making. The government should provide policy and financial incentives to those who perform well in the pilots and promptly summarize replicable experiences for national promotion. Through "piloting first, then promoting," the risks of large-scale implementation can be reduced, and the details of the mechanism can be gradually improved.

(5) Improve the Standard and Certification System: At the policy level, support the National Standardization Management Committee to take the lead in accelerating the development of semantic-related standards. The aforementioned innovation semantic indexing specifications should be elevated to national standards and even international standards. In particular, the conceptual definition of the DIKWP model itself and the format of semantic data need to be published in official standard documents. The government should also encourage domestic active leadership or participation in the formulation of international standards. For example, proposing Chinese solutions such as "semantic blockchain interoperability standards" and "semantic AI white-box evaluation standards" within the framework of ISO or ITU. At the same time, for mature international specifications, such as the W3C's semantic web standards and knowledge graph exchange specifications, they should be promptly transformed into national standards to ensure domestic and foreign compatibility and openness. In addition to standards, it is also necessary to establish a certification and evaluation system. It is recommended to support third-party testing agencies to conduct "semantic sovereignty compliance assessments" for enterprises or platforms. The assessment content would include: whether the AI system has semantic bias, whether the management of data and knowledge assets meets semantic security requirements, and whether the innovation rights confirmation and distribution mechanism is sound. Those who pass the certification can be given a "semantic-friendly enterprise" certification mark and be given preferential treatment in market access or government procurement, which will incentivize the industry to pay attention to semantic governance.

(6) Optimize Industrial Ecosystem Incentives: From the perspective of industrial policy, guide enterprises to engage in the construction of a semantic innovation ecosystem. Specific measures: provide preferential treatment in taxation, financing, etc., to enterprises that develop core software and hardware such as semantic blockchain platforms, DIKWPaaS platforms, and knowledge graph tools. For example, give preferential treatment to enterprises in the semantic technology field when identifying high-tech enterprises, providing tax reductions and exemptions; establish a special fund for semantic technology, with government investment and guidance funds to support start-ups and project incubation. In addition, establish a semantic technology innovation award to commend units and individuals who have made outstanding contributions to the practice of semantic sovereignty. This is similar to the Science and Technology Progress Award but focuses on the semantic field to enhance the recognition of semantic innovation in the industry and the public. For leading enterprises, encourage and require them to play a leading role, such as opening up Chinese knowledge graphs and large model interfaces for use by small and medium-sized enterprises, creating an ecosystem where large, medium, and small enterprises develop together. The open initiatives of leading enterprises can be promoted through policy guidance (for example, by including them in corporate social responsibility or as a condition for evaluation and excellence). This will form a "big hands pulling small hands" situation, allowing new start-ups and individual developers to obtain semantic resources and participate in innovation competition in a fair environment.

(7) Promote International Semantic Docking and Cooperation: The digital world has no borders, and the semantic innovation ecosystem also needs global collaboration. China can proactively bring the issues of semantic sovereignty and semantic fairness into the international dialogue on digital governance. For example, at venues such as the United Nations, G20, and BRICS, advocate for the formulation of "global principles of semantic fairness" or action plans, clarifying that all countries have the right to maintain their own national semantic space, while all countries should jointly oppose algorithmic discrimination and protect semantic diversity. Strengthening cooperation in semantic technology and standards with countries along the "Belt and Road" is also a feasible approach. This includes: co-building multilingual knowledge bases, jointly holding forums on sovereign AI and semantics, and establishing transnational semantic research alliances. Through this cooperation, China's "circle of friends" in the semantic field can be expanded, and our discourse power can be enhanced. We should also strive for a greater say in international standards organizations, recommend Chinese experts to serve as leaders of relevant working groups, and actively propose standard proposals led by China. This is especially true for taking the lead in the formulation of standards for emerging fields such as semantic blockchain and semantic communication. Once Chinese solutions are adopted internationally, it will not only enhance our soft power but also provide standard convenience for our enterprises to go global. Another level of the international semantic docking mechanism is specific technical docking: for example, promoting the interconnection of Chinese and foreign knowledge graph data, and the alignment of large models of different languages. The government can fund research projects on "bilingual/multilingual semantic alignment" and jointly develop cross-lingual semantic interoperability tools with other countries. This will not only help us acquire global knowledge but also spread Chinese cultural semantics to the world, which is a win-win situation.

(8) Improve Legal and Regulatory Guarantees: Finally, legislative work needs to follow up to support the aforementioned policies. It is recommended that when revising the "Cybersecurity Law," "Data Security Law," and the future "Artificial Intelligence Governance Regulations," provisions concerning the accurate expression of language, characters, and cultural content be added, incorporating semantic security into the scope of national security. This can provide a legal basis for combating malicious tampering with others' innovative achievements and the dissemination of false semantic information. In the field of intellectual property, revise the "Copyright Law" and "Patent Law" or issue judicial interpretations to clarify the ownership, licensing methods, and benefit-sharing rules for knowledge graphs and AI-generated content. For example, for AI-generated content, it can be stipulated that the copyright is shared between the developer and the data subject who provided the materials according to a certain proportion; for human knowledge extracted by knowledge graphs, a usage license system can be explored to allow knowledge contributors to receive returns. In addition, the explicit definition of data and knowledge as asset types for which rights can be confirmed also requires legal confirmation. At the local level, supporting regulations can be formulated to refine the implementation. For example, a certain province or city could require that the decision-making process of government AI must be auditable, and that if AI is used to assist in major administrative decisions, the logic must be publicly explained. These regulations can force public departments to take the lead in practicing semantic transparency and responsibility traceability, setting an example for the whole society. The improvement of the law not only protects existing rights and interests but also leaves room for new problems that may arise in the future—such as cutting-edge issues like the semantic rights of "artificial consciousness." Preparing conceptual definitions and principles in the legal framework in advance will allow us to remain at ease in the face of new technological waves.

Conclusion: Based on the above discussion, it can be seen that in the future semantic-enabled digital world, the establishment and pricing of innovation will no longer be limited to the traditional model but will be reconstructed at a deep semantic level. Guided by Professor Yucong Duan's theories of symmetrical economics, the DIKWP model, and semantic sovereignty, we have depicted a relatively complete system from concept to practice: characterizing innovation through semantic modeling, establishing rights and interests through semantic sovereignty, measuring value through semantic metrics, exchanging fairly through symmetrical mechanisms, and verifying its feasibility in multiple scenarios. Of course, the construction of this system still faces many challenges, such as the maturity of semantic analysis technology, the change in the concepts of all parties, and the complexity of international cooperation. But the opportunities are also huge—mastering the leadership in semantic innovation will put us in a favorable position in global digital governance and the new round of technological competition. Therefore, it is necessary to be prepared at the policy level and accelerate the layout. Only by consolidating semantic sovereignty under the sovereign AI framework and establishing a healthy semantic innovation ecosystem can we ensure that in the digital age, we both encourage a hundred flowers of innovation to bloom and maintain a fair and orderly distribution of value, laying a solid foundation for achieving the strategic goal of a "Digital China" and building trustworthy artificial intelligence.

References:

·Duan, Yucong, et al. Construction of a Semantic Sovereignty System from the Perspective of Sovereign AI. July 2025.

·Duan, Yucong, et al. Business Value Assessment Report of the DIKWP Semantic Model. March 2025.

·Duan, Yucong, et al. A Survey of Personalized Semantic Secure Communication Based on the DIKWP Model. August 2025.

·Duan, Yucong. "From Conceptual Space to Semantic Space: The AI Paradigm Shift Led by the DIKWP Model." ScienceNet Blog, 2025.

·Duan, Yucong. "A Mental Perspective on the Economics of Artificial Consciousness and the Study of Cognitive Asymmetry." Zhihu Column, 2024. (Explains the significance of symmetrical economics in the age of artificial intelligence).

·Duan, Yucong. Sovereign AI-based Semantic Sovereignty Large Models and High-Quality Datasets. Shandong Big Data Research Association, 2025. (Discusses semantic value measurement and the principle of semantic fairness).

·Duan, Yucong. "DIKWP Model: Deciphering the Core Elements of Digital Assets." ResearchGate Preprint, 2024. (Introduces the application of DIKWP in organizational knowledge management).

·Duan, Yucong. "Semantic Sovereignty in Global Digital Governance: Opportunities and Challenges." ScienceNet Blog, 2024. (Emphasizes the diversity of cultural semantics and international cooperation).

·National Standardization Management Committee. Semantic Blockchain Technology Reference Architecture. National Standard for Soliciting Comments, 2025. (Proposes the implementation framework of DIKWP on the blockchain).

Citation Sources:

·(PDF) Business Value Assessment Report of the DIKWP Semantic Model, https://www.researchgate.net/publication/390311595_DIKWPyuyimoxingshangyejiazhipinggubaogao

·Sovereign AI-based Semantic Sovereignty Large Models and High-Quality Datasets - Zhihu Column, https://zhuanlan.zhihu.com/p/1924861404294411092

·Original text: Sovereign AI-based Semantic Sovereignty Large Models and High-Quality Datasets, https://www.sdbdra.cn/newsinfo/8531201.html

·(PDF) A Survey of Personalized Semantic Secure Communication Based on the DIKWP Model, https://www.researchgate.net/publication/394588750_jiyu_DIKWP_moxingdegexinghuayuyianquantongxinzongshu

·(PDF) Construction of a Semantic Sovereignty System from the Perspective of Sovereign AI, https://www.researchgate.net/publication/393461385_zhuquanAIshijiaoxiadeyuyizhuquantixijianshe

·ScienceNet—Semantic Sovereignty Empowering the Digitization, Intelligence, and ... of the Hainan Free Trade Port's Independent Customs Operations, http://https--blog--sciencenet--cn.proxy.xianning.gov.cn/blog-3429562-1490959.html

·Analysis of the Complexity of Artificial Consciousness Systems Based on the Partial Convertibility of DIKWP and the Relativity of Consciousness, https://wap.sciencenet.cn/blog-3429562-1488725.html?mobile=1

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