[TL;DR]
- The current knowledge economy has a structural problem in which platforms like Google and Wikipedia monopolize profits by exploiting the knowledge of individual experts.
- Knowledge graphs tokenize personal expertise into NFTs, ensure accuracy through decentralized verification systems, and provide token rewards based on contributions.
- In fields such as healthcare, education, and information curation, experts can monetize their knowledge without spatial or temporal constraints, while users gain access to cheaper and more accurate information.
1. Structural Limits of the Existing Knowledge Economy: Information Monopoly and Value Extraction
1.1. Big Tech’s Monopoly on Knowledge: The Real Value Captured by Google and Wikipedia
Looking closely at the knowledge ecosystem we use every day, we can find a striking contradiction. The value created in the process where billions of people ask questions and seek answers worldwide is converted entirely into the profits of a few platform corporations. Google analyzes users’ search queries and click patterns to generate hundreds of billions of dollars annually in ad revenue, but the individual experts and content creators who actually produce and curate knowledge receive almost no proper rewards.
Wikipedia presents an even starker case. Tens of thousands of volunteer editors worldwide freely build and maintain a vast knowledge database, yet the economic value created when this accumulated knowledge is used in Google search results or AI model training is monopolized entirely by Big Tech corporations. Editors receive no financial rewards for their contributions, providing labor solely under the guise of “honor” or “public service.”
The most problematic aspect of this structure lies in the information asymmetry that arises in the production and distribution of knowledge. Google exclusively holds all the data on which content is most searched and clicked, and which keywords maximize ad revenue. Meanwhile, the individual creators and experts producing the actual content have no way of knowing how valuable their content really is, nor how it is being monetized.
Moreover, the opacity of algorithms exacerbates adverse selection problems: high-quality expertise gets buried, while low-quality content optimized for SEO rises to the top. Clickbait health tips outrank accurate medical advice written by doctors, and superficial information aimed at generating ad revenue appears before practical guidance based on years of professional experience. Such distorted incentives accelerate the overall decline in the quality of the knowledge ecosystem.
1.2. Undervaluation of Expertise and Dependence on Platforms
The biggest problem for individuals with real expertise in today’s knowledge economy is the near absence of direct, independent pathways to monetize their knowledge and experience. A software engineer with 20 years of experience must rely on platforms like YouTube or blogging services to share technical know-how. A doctor wishing to provide medical information must comply with the rules and algorithms of existing health information sites. Even a lawyer from a law firm offering legal consultation has their earnings determined by the platform’s fee structures and exposure policies.
This platform-centric structure pressures experts to standardize and simplify their knowledge. Even in areas requiring nuanced professional judgment, content must be reduced to simple tips or guides consumable by the public to be recognized by platforms. In legal consultation, the particularities of individual cases and the complexity of case law are important, yet uniform content like “5 Steps for Property Division in Divorce” garners higher traffic.
The depth and context of expert knowledge are continuously diluted by the commercial logic of platforms. Worse still, accumulated expert contributions become the property of platforms, leaving no lasting value for the individual experts themselves. Years of answers and content become platform-owned, preventing experts from transferring their reputation and accumulated contributions when moving to other platforms or seeking independence.
This dependence weakens experts’ bargaining power and makes them vulnerable to sudden platform policy changes. A Google search algorithm update can drastically reduce traffic to an expert blog that long held top ranking, and YouTube’s revenue-sharing adjustments can arbitrarily cut creators’ income. Experts, despite the inherent value of their knowledge and experience, are trapped in structural vulnerability, forced to endure economic instability dictated by platform whims.
1.3. Verification and Reliability Problems: Fake News and Biased Algorithms
Another fundamental limitation of centralized knowledge platforms is the lack of transparent and fair mechanisms to ensure the accuracy and reliability of information. How Google determines search result rankings, how Wikipedia resolves editorial disputes, and how YouTube prioritizes content in recommendations remain entirely opaque. This opacity becomes fertile ground for manipulation and bias.
Actors with economic or political interests can manipulate search results or Wikipedia entries to their advantage via SEO manipulation or mass editing. Ordinary users lack the information or tools to judge such manipulations and are structurally vulnerable to trusting distorted information.
The spread of inaccurate information in specialized domains can cause severe societal harm. During the COVID-19 pandemic, unproven treatments and preventive measures spread rapidly on social media, endangering public health. Financial losses from misleading investment information, legal disputes from erroneous legal advice, and system failures caused by inaccurate technical knowledge are already everyday occurrences.
In the current system, accountability for such misinformation is unclear. Platforms evade responsibility by framing themselves merely as “information facilitators,” while individual producers of information often remain anonymous or untraceable. Furthermore, experts providing accurate information must compete on equal footing with misinformation, perpetuating a cycle where sensational or bias-reinforcing content attracts more attention than accuracy.
2. A New Knowledge Paradigm Proposed by the Knowledge Graph
2.1. NFT-ization of Personal Knowledge and the Establishment of Ownership
The fundamental solution that has emerged to address the structural problems of the existing knowledge economy is a system that establishes clear ownership of all knowledge and information produced by individuals through blockchain. Medical information written by a doctor, legal consultations provided by a lawyer, and technical know-how shared by an engineer are all recorded as unique digital assets, allowing the creator to retain permanent ownership. This system goes beyond merely protecting copyright to create a new economic model in which all value generated through the use and distribution of knowledge is continuously returned to the original creator.
In the existing system, once experts uploaded content to a platform, all subsequent utilization and monetization became the platform’s property. However, in a knowledge NFT system, whenever that knowledge is cited or utilized by others, the original creator automatically receives a percentage of royalties. For example, a guideline on diabetes management written by a doctor generates economic rewards for the creator whenever it is cited by other medical professionals, used in AI model training, or adopted as educational material.
This ownership model fundamentally changes the economic incentives of knowledge producers. Since high-quality content continues to generate revenue as it is used by more people over time, experts are motivated to invest in knowledge that will remain useful in the long run rather than creating clickbait content for immediate views. Furthermore, the act of systematically documenting and structuring one’s expertise itself becomes a long-term asset-building activity, encouraging experts to actively share their experience and know-how.
This shift directly contributes to qualitative improvements in knowledge production. No longer pressured to craft sensational titles or oversimplify content for platform algorithms, experts can produce in-depth knowledge that fully reflects their expertise and experience. As a result, knowledge consumers gain access to more accurate and practical information, while knowledge producers receive fair rewards commensurate with their contributions.
2.2. Decentralized Knowledge Verification and Trust Systems
If the establishment of knowledge ownership provides the incentive for production, decentralized verification systems form the core mechanism ensuring accuracy and reliability of information. In blockchain-based knowledge graphs, a decentralized peer review system is established in which multiple experts within a field verify and evaluate each other’s knowledge. For medical information, multiple healthcare professionals review accuracy and timeliness; for legal consultation, other lawyers assess the appropriateness of case law and statutory application; and for technical documents, fellow engineers verify practicality and correctness through layered review processes.
Experts participating in this verification process also receive token rewards for their evaluative contributions, motivating them to engage actively in quality control of the knowledge ecosystem beyond mere content consumption. Those who consistently provide accurate and helpful evaluations gain higher trust scores, which in turn lead to more verification opportunities and rewards in a merit-based system.
The greatest strength of this decentralized verification is the ability to ensure objective evaluation free from the biases of any single institution or platform. Unlike the editorial disputes on Wikipedia or the controversies around manipulated Google rankings, verification processes recorded on the blockchain are transparently disclosed, allowing anyone to see which expert evaluated the information and on what grounds. Since the past accuracy and expertise of reviewers accumulate into trust scores, the system evolves into a more accurate and reliable verification mechanism over time.
Experts who consistently provide accurate evaluations in a specific domain gain greater influence, while participants who repeatedly provide inaccurate assessments naturally see their influence diminish. This self-regulating mechanism effectively filters out malicious manipulation or biased evaluations while simultaneously creating a fair environment in which individuals with true expertise gain influence and rewards proportionate to their competence.
2.3. Token Reward Mechanisms Based on Knowledge Contributions
The economic foundation supporting knowledge ownership and decentralized verification systems is a sophisticated incentive structure that measures individual contributions precisely and provides token rewards proportionally. Instead of one-off compensation for uploading content, rewards are distributed continuously based on how useful the knowledge has been, the evaluations it has received from other experts, and the degree to which it has contributed to solving real-world problems. For example, a programming guide written by a software engineer that is frequently referenced by other developers and helps solve real problems will continue to generate additional token rewards beyond the initial creation reward.
When medical information provided by a doctor is validated by other healthcare professionals and proves beneficial in actual patient treatment, rewards are given according to the impact achieved. This performance-based reward system encourages experts to provide practical information that goes beyond mere knowledge sharing to actually solving problems.
Another important aspect of token rewards is the incentive to connect and integrate knowledge. Contributors who link knowledge across domains to generate new insights or present comprehensive solutions to interdisciplinary problems receive additional rewards. Examples include healthcare solutions combining medicine and IT, smart contract design integrating law and blockchain, and learning methodologies blending education and psychology.
Such a multi-layered reward system promotes natural division of labor and specialization within the knowledge ecosystem. Those who excel at producing original knowledge, those who specialize in verifying and curating existing knowledge, and those skilled at connecting and integrating diverse knowledge all contribute according to their strengths and receive appropriate rewards. This structure transcends today’s uniform content-production-focused system to create a more diverse and enriched knowledge ecosystem.
Furthermore, this tokenomics enables long-term accumulation of knowledge value. Knowledge and experiences accumulated by experts in their younger years continue to generate revenue later in life, and when younger professionals build upon that knowledge to produce more advanced information, the original creators also share in the outcomes. Ultimately, all participants receive fair rewards throughout the entire cycle of knowledge production, verification, and utilization, while society as a whole benefits from enhanced levels of knowledge and problem-solving capacity.
3. Application Scenarios of the Knowledge Graph by Sector
3.1. Tokenization of Expert Knowledge: New Revenue Models for Doctors, Lawyers, and Engineers
One of the greatest constraints faced by professionals today is the physical limitation of time and space, which makes it difficult to monetize their expertise in a scalable way. Even the most skilled doctor can only treat a limited number of patients per day, and experienced lawyers can only handle a finite number of cases simultaneously. Within a knowledge graph system, however, such professionals can systematically tokenize their experience and know-how to deliver value globally without spatial or temporal restrictions, while generating continuous revenue.
For instance, if a cardiac surgeon with 30 years of surgical experience tokenizes a document titled “Complications and Countermeasures in Complex Heart Surgeries” as an NFT, young doctors worldwide can reference and apply it in real surgical settings, each time rewarding the original creator with tokens. Similarly, a patent lawyer who compiles “Legal Pitfalls to Avoid in Global Patent Applications” and whose guidance helps startups prevent disputes receives additional rewards proportional to the value created.
The key point in this model is that expert knowledge functions not merely as information provision but as a tool for solving real problems. A systems architect with 20 years of experience who documents how they solved scalability challenges in large-scale services can tokenize this knowledge, allowing other companies facing similar problems to apply it, reduce months of trial and error, and cut development costs. The expert gains ongoing income from a single knowledge contribution, while the companies benefit from affordable access to proven expertise—a win-win structure.
Furthermore, as these pieces of knowledge interconnect and combine, collective intelligence solutions can emerge for complex problems too challenging for individual experts to solve alone. Developing medical AI, for example, requires clinical experience from medical experts, algorithm design skills from data scientists, and system architecture know-how from software engineers. With tokenized contributions from each domain and shared distribution of the resulting outcomes, a new form of collaboration becomes possible.
3.2. Crowdsourced Educational Content: A Decentralized University Built by Individuals
While the tokenization of expert knowledge focuses on monetizing individual expertise, the application of knowledge graphs in education aims to dismantle the monopoly of traditional educational institutions and create a global learning network where everyone can access quality education. Currently, lectures from prestigious universities and top experts’ programs remain accessible only to a few due to high costs and geographic constraints. In contrast, on decentralized education platforms, world-class educators can tokenize their lectures and connect directly with learners worldwide.
A machine learning course by a Stanford AI researcher, a quantum computing class by an MIT professor, or anatomy lab videos from Harvard Medical School can all be tokenized individually, allowing learners to selectively enroll based on their needs and interests. Unlike traditional package-style degree programs, learners can construct personalized learning paths to acquire only the knowledge they truly need.
What’s more interesting is that these individual educational contents can interconnect to create more flexible and practical curricula than those of existing universities. For example, a data science learner could combine a statistics lecture from a Seoul National University professor, programming practice from a Google engineer, and business applications from a McKinsey consultant’s case study, building a more practice-oriented and up-to-date curriculum than any single university could offer.
The core of this decentralized education model is a reward system linked to learning outcomes. It is not merely about attending lectures; whether the knowledge is truly acquired and applicable in practice is verified and recorded on the blockchain. Learners who demonstrate excellent performance receive token rewards. Additionally, if learners share new insights or practical results discovered during their learning process with others, they receive extra rewards, transforming education from one-way knowledge delivery into a process of mutual learning and knowledge creation.
3.3. Real-Time Information Curation: Monetizing the Value of Citizen Journalists and Eyewitnesses
While structured delivery of knowledge is essential in education, real-time relevance and on-the-ground presence are the core values in news and information curation. In traditional media systems, a small number of journalists and editors monopolized information production and distribution. In contrast, within a knowledge graph-based ecosystem, every individual at a scene can act as a citizen journalist, recording events and situations in real time and receiving token rewards proportional to the value of their information.
For example, if a citizen witnesses a traffic accident and uploads photos and videos with precise time and location data, insurance companies, news outlets, or traffic authorities that use the information will provide token rewards. If someone present at a natural disaster site shares real-time updates on damage, which in turn aids rescue operations or evacuation planning, they receive compensation proportional to their social contribution.
The most important aspect of this system is the mechanism ensuring the reliability and accuracy of information. A multi-layered verification process checks whether eyewitness reports align with other testimonies or official data, and whether photos or videos have been manipulated. Citizen journalists who consistently provide accurate and useful information earn high trust scores, which translate into greater opportunities to provide critical information and higher rewards—a reputation-based system in action.
Moreover, this decentralized information collection system captures local and detailed stories often overlooked by mainstream media. Small regional issues or subtle changes in specialized fields, which major outlets might ignore, can now be documented and shared directly by residents or domain experts. As a result, diversity and accessibility of information increase dramatically, while all individuals contributing to information production receive fair rewards, forming a new media ecosystem.
4. The Technological Infrastructure Supporting the Knowledge Graph Protocol
4.1. Wallet-as-a-Service (WaaS): A User Interface That Hides Complexity
For experts to tokenize their knowledge, educators to mint their lectures as NFTs, and citizen journalists to provide real-time information and gain economic benefits, it is essential that they can do so without being conscious of the underlying complexity of blockchain technology. This is precisely where Wallet-as-a-Service (WaaS) plays a crucial role, acting as the bridge between the knowledge graph and everyday users. Experts who want to contribute knowledge, or individuals who want to participate in education, should not need to know anything about private key management or gas fees—they should be able to access blockchain-based services with the same ease as using a mobile app.
The most important element of WaaS is the complete abstraction of blockchain wallet creation and management through social login. Unlike the traditional method, where a doctor who wanted to participate in knowledge sharing had to memorize seed phrases and securely store private keys, in WaaS they simply log in with a Google or Kakao account, and a wallet is automatically created and managed in the background. Professors creating educational content or individuals providing real-time information can use all services naturally without needing to understand blockchain or cryptocurrency concepts.
The integration of multi-chain asset management is another core value provided by WaaS. Currently, knowledge graph services are spread across multiple blockchains—Ethereum, Polygon, Solana, and others—forcing users to manage separate wallets and handle token transfers across chains. Through WaaS, however, users can manage tokens earned from contributing medical knowledge, rewards for educational content, and profits from information curation all within a single interface.
Users no longer need to worry about which chain holds which tokens, how much the network fee costs, or how to transfer assets across chains. WaaS automatically finds the optimal route and processes the transaction. This abstraction enables seamless interaction across different domains in the knowledge graph ecosystem. For example, tokens earned by providing medical knowledge can be invested in educational services, or rewards from information curation can be used to pay for expert consultations—all with just a few simple taps, without any technical hurdles.
4.2. AI-Powered Knowledge Linking and Automatic Curation
While WaaS improves accessibility to the knowledge graph ecosystem, AI-powered knowledge linking systems organize vast amounts of individual knowledge into meaningful networks and help users efficiently find the exact information they need. If tens of thousands of experts’ contributions are simply listed, users may easily become lost in a sea of information. Advanced AI algorithms identify semantic relationships among knowledge pieces, automatically classifying and linking them, transforming individual contributions into an integrated knowledge system with greater value.
For example, medical information uploaded by a cardiac surgeon about surgical techniques, specifications provided by a biomedical engineer for new medical devices, and rehabilitation guidelines written by a recovery specialist can all be automatically linked by AI to form a comprehensive knowledge package covering the entire treatment process. When a user asks about a specific medical issue, the AI synthesizes all relevant expert knowledge to provide a personalized answer, distributing token rewards proportionally to each contributing expert.
Another key function of this AI curation system is continuous monitoring of reliability and timeliness of knowledge. In fast-developing fields such as medicine, information from a few years ago can already be outdated, while in law, new precedents or legislative changes can render existing knowledge inaccurate. AI systems monitor new research results and policy changes in real time, assess the validity of existing knowledge, and alert experts when updates are needed.
AI also learns from users’ search patterns and feedback to provide personalized knowledge recommendations. By analyzing the past usage history of a student studying a particular subject or an expert solving a specific problem, the system proactively suggests knowledge they are likely to need next or valuable information they have not yet discovered. Through this intelligent curation, the knowledge graph evolves from a mere repository of information into an intelligent knowledge ecosystem that actively learns and grows.
4.3. Interoperability and Licensing of Knowledge Assets
If AI provides the technological foundation for linking and curating knowledge, interoperability and licensing systems form the legal and technical framework that allows knowledge assets distributed across different platforms and blockchains to be freely combined and utilized. Unlike today, where each knowledge platform operates independently and knowledge transfer or integration across platforms is difficult, blockchain-based knowledge graphs achieve true interoperability through standardized protocols that ensure any knowledge created on one platform can be recognized and used on another.
For instance, a medical knowledge NFT generated by a doctor on Platform A can be used in an educational service on Platform B or in AI model training on Platform C, with the creator’s ownership and revenue share automatically guaranteed. This cross-platform compatibility frees knowledge producers from being locked into specific platforms, empowering them to choose optimal services. At the same time, knowledge consumers gain access to more diverse and enriched sources of information.
The licensing system is the core mechanism supporting such interoperability. Each knowledge asset can be minted with clearly defined licensing conditions, including whether commercial use is allowed, conditions for derivative works, and revenue-sharing ratios. For example, when a doctor shares clinical experience, they could set conditions like “free for educational use only, 30% of profits required for commercial use.” These conditions are then automatically enforced by smart contracts throughout all utilization processes.
This flexible licensing greatly enhances knowledge usability while protecting creators’ rights. Researchers may freely access knowledge for academic purposes but must pay fair licensing fees for commercial product development. Educators may provide free use for public education while sharing revenue for paid courses. Moreover, such standardized licensing systems make possible the emergence of a global knowledge marketplace, where expert knowledge transcends borders to find optimal applications—realizing a truly global knowledge economy.
5. Challenges of the Knowledge Graph and the Future of a Human-Centered Knowledge Economy
5.1. Resistance from Existing Platforms and Regulatory Barriers
Despite the fundamental transformation proposed by the knowledge graph, several real-world barriers must be overcome before this paradigm can be fully realized. One of the biggest challenges is the strong resistance from existing knowledge platform corporations. It is unrealistic to expect that companies like Google, Wikipedia, and educational platforms—who have monopolized the knowledge economy for decades—will suddenly give up market share to decentralized networks of individuals. They are likely to lobby regulators to restrict knowledge graph services or introduce their own token-based reward systems to lock in existing users.
They may also leverage their massive capital reserves to pressure emerging knowledge graph projects through temporary dumping or offering free services. Google, for instance, might manipulate search algorithms to suppress exposure of decentralized knowledge platforms, while traditional educational platforms could preemptively secure exclusive contracts with renowned professors to maintain control over content.
Regulatory uncertainty and legal complexity also present major obstacles. Questions remain unresolved about how existing tax laws, medical regulations, or attorney laws apply when individuals sell their expertise as NFTs or receive token rewards. Similarly, it is unclear whether certificates or diplomas issued by decentralized education platforms would be legally recognized. The perspective governments adopt toward knowledge graphs and the regulatory frameworks they establish will heavily influence the speed and direction of ecosystem development.
Furthermore, variations in data protection and data sovereignty laws across countries add complexity to providing global knowledge graph services. Europe’s GDPR, China’s data localization requirements, and the U.S. Cloud Act could all restrict the free cross-border flow of knowledge assets, posing challenges to realizing a truly global knowledge economy.
5.2. Technical Maturity and User Experience Limitations
Alongside regulatory barriers, another key consideration is the current maturity of technology and limitations in user experience. Today’s blockchain infrastructure still faces challenges in scalability and processing speed to support global participation of experts and learners in knowledge graph services. High gas fees and slow transaction speeds on Ethereum, as well as relatively weaker security and decentralization on other blockchains, remain barriers to mass adoption. For ordinary users, the learning curve required to fully understand and navigate tokenomics and smart contracts is still steep.
Another challenge is minimizing the negative impact of token price volatility on real-world knowledge services. If rewards for experts fluctuate drastically with token market prices, it becomes difficult to maintain stable incentives for knowledge production. Solutions such as stablecoin-based reward systems or mechanisms to buffer volatility are necessary, but these technologies require time to mature and stabilize.
AI algorithms for knowledge quality assessment and trust measurement also remain in development. In specialized fields like medicine or law, subtle judgments and contextual understanding are essential, but current AI systems struggle to capture such complexities. Inaccurate AI evaluations could result in misleading information receiving high scores while valuable expertise is undervalued, highlighting the need for continuous technological improvement.
5.3. Societal Changes Brought by a Human-Centered Knowledge Economy
Despite these challenges, the core value proposition of the knowledge graph suggests that these obstacles will eventually be overcome, ushering in a radically new form of knowledge society. An economy in which individuals can profit from their expertise, earn additional income through everyday information curation, and receive rewards for contributions in learning processes is clearly more fair and efficient than the current system. Initially, early adopters and pioneering communities will begin implementing knowledge graphs, and as they demonstrate superior service quality and economic benefits, adoption will gradually expand.
One of the most important aspects of this transformation is the dramatic improvement in knowledge accessibility. Expert consultations and world-class education, currently restricted to a privileged few due to high costs, can be delivered at much lower costs through token economies. A student in a developing country could take a lecture from a Harvard professor, a rural patient could receive advice from an urban specialist, and a small business could access consulting expertise comparable to that of global firms.
Furthermore, this change will bring about a complete fusion of the physical and digital worlds. All professional activities, learning experiences, and even everyday informational contributions will acquire economic value through blockchain, turning the real world itself into one massive knowledge economy system. Accurate information provision earns trust tokens, excellent teaching brings educational rewards, and sharing creative ideas generates knowledge creation income—every positive intellectual activity connects to immediate economic feedback.
Ultimately, the knowledge graph will realize a paradigm in which personal intellectual assets become social infrastructure, and everyday learning and knowledge sharing become forms of economic contribution. All intellectual resources worldwide will be interconnected in a single network transcending national and corporate boundaries, enabling humanity to solve complex challenges through collective intelligence and distributed expertise. Grand challenges such as climate change, pandemic prevention, and reducing social inequality will no longer depend solely on unilateral plans by governments or corporations but will be addressed through self-organized knowledge systems powered by the voluntary participation of billions of individuals.
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