At some point in the evolution of human knowledge, the use of papers, slates, and bamboo slips was replaced by more efficient means. One of them is the Internet. And everybody loved it. Everybody except machines, that is. As it stands, conventional forms of human knowledge like videos, texts, and pictures can’t be assimilated by machines. This present generation of Artificial Intelligence can identify but cannot understand the way a human would. Where a kid can tell if an animal is a hamster or not, AI depends on loads of data which will then be supplied with pattern matches before identification can occur.
The development of Artificial Intelligence can be split into four:
- Computational Intelligence: This allows machines to not only save data but also calculate data.
- Conscious Intelligence: Which allows machines to learn by themselves and remember what they learned.
- Cognitive Intelligence: which allows machines to learn as well as process information.
- Perceptual Intelligence: which allows machines to see, identify things, listen and speak.
Currently, giant strides have been taken to design Artificial Intelligence that is capable of face and voice recognition, perceptual intelligence, and so on. The next task on the horizon is cognitive intelligence which would allow AI to understand as well as process information. This information will be in a clearly defined structure to allow efficient processing by AI.
The issue of processing used to be a problem until Google broke the record. Thanks to Google, machines can now understand texts, pictures, and videos through the knowledge graph. This genius innovation was introduced in 2012. Here is how the knowledge graph works. It makes use of structured relationships, entities, and structures as well as other useful elements to represent human knowledge. This is how they can understand and interpret data.
This seemingly small innovation has set the pace for the advancement of Artificial Intelligence in this day and age. However, utilization of KG by AI is not quite smooth just yet. This process still has some hurdles to scale. They are:
- Potential data manipulation under a centralized system.
- Time-consuming conversion of data to required formats.
- Heavy labor input for performance
The absolute documentation of every single human knowledge cannot in any way be successfully achieved by one country, company, or organization. Even if this has been achieved, construction of the average knowledge graph is a lot of hard work which can take ages as this process includes processing knowledge after extraction, updating existing knowledge, and fusion.
Finally, constructing knowledge graphs, is super expensive which is why not a lot of strides have been made in the industry. Because most companies rely on third-party cognitive graphs, their data is often exposed to nefarious tampering.
Knowledge Graphs have a lot of similarities with the bionic design of the neuron design of the human brain. The connection of nodes in knowledge graphs is very much like the connection of neurons in the human brain. The smartness of knowledge graphs is determined by how dense the connection of nodes is.
What role does Epik Protocol have in all of this?
Epik Protocol aims to use blockchain technology to build a highly decentralized KG that will revolutionize the face of AI technology. Epik Protocol will be tapping into Filecoin’s decentralized storage technology to do this.
What differentiates Epik Protocol from Filecoin?
Filecoin is in the process of defining a decentralized storage framework and as a general storage framework comes in contact with different storage application circumstances, Filecoin has focused on the storage of huge files under very limited throughput.
This is why Epik Protocol is relying on Filecoin to secure trusted storage. The central technology which makes up Filecoin is the IPFS protocol. IPFS protocol is a peer-to-peer network for sharing and also storing data in any distributed system.
What IPFS protocol does for devices is that it assembles devices into a unified file system and processes these files accurately into a Merkle Trie structure hence forming a flawless Root Hash. With this in place, duplicate data blocks will not be stored and nodes will simply integrate with Root Hash to get consistency.
One thing IPFS technology lacks however is an anti-fraud mechanism and an incentive mechanism. Filecoin found a way to troubleshoot this problem by improving on IPFS. They did this in two ways. First, they designed Proof-of-Storage to incentivize their ecosystem and then they fused Zero-Knowledge Proof to design Proof-of-Spacetime to stop frauds (PoSt) and Proof-of-Replication (PoRep).
Another exceptional thing Filecoin did is to enable foreign parties to organize their idle machines by themselves into a storage system that is both distributed and unified without needing to trust each other. This process is quite unlike your regular distributed database. In this case, all parties are allowed to keep and access data under consensus and in a permissionless manner.
They will also be utilizing Token Economy which guarantees fair incentives, Decentralized Financial Tech, and finally, Decentralized Autonomous Organization. In the aspect of reliable storage, Epik Protocol’s KG collaboration network is made up of small bin-log files to allow collaboration that is both effective and efficient. This is a welcome change from large file storage.
To ensure smooth integration between platforms, Epik Protocol leverages Filecoin’s core technology to design a Filecoin Layer 2 storage network for KG to collect data and KG collaboration network on Layer 2 using more than one incentive. What happens, in this case, is that the KG data is then aggregated consistently into big snapshot files before permanent storage in Filecoin’s network.
Epik Protocol Layer 2 network successfully secures the trust Filecoin Layer 1 network as they stay focused on the collective efforts of constructing KG data belonging to various domains stored as bin-log files in the Epik network. When there is no permission from a centralized entity, everyone who is anyone can download the bin-log files and carry out commands to restore the graph database locally. One more thing users can do is to upload the database snapshot onto the Filecoin network to earn some cool rewards.
In terms of incentive, Epik Protocol’s supreme Token Economy provides the Epik ecosystem with a trusted incentive model because Tokenization develops a fresh way for blockchain-based value technology to be transferred. There are two things to keep in mind here about tokens and transactions. They include:
- Tokens can be exchanged, transferred, and spent.
- Transactions are immutable and transparent
Tokenization is so unique that it can be utilized as a transfer of value with low costs without having to depend on third-party verifications. Without this third-party reliance, tokenization is a super programmable asset and with Epik Protocol’s native token, EPK in circulation, the foundation of Epik’s Token Economy can be built.
One smart thing Epik Protocol does is to forge collaborative relationships for core participants of the KG network while driving towards their interests. The purpose of this is to collectively create a knowledge graph.
Epik Protocol’s core participants are:
- Knowledge Gateways
- Domain Experts
- Knowledge Node
- Bounty Hunters
Each of these core participants has their responsibilities in their collaboration with Epik Protocol.
Knowledge Gateways (KGs)
Knowledge Gateways are the channels through which users can obtain current knowledge graph data. Knowledge Gateways have the duty of staking EPK to access knowledge graph data.
It is estimated that as demand for Epik’s knowledge graph data increases, more EPK tokens will be staked by both Knowledge Gateways and Knowledge Node. What this does is that it altogether ramps your demand for the value of EPK tokens and EPK tokens themselves.
Moving forward, all accrued rewards will be automatically distributed via smart contracts. In the absence of manual intervention, all rewards and transactions from multiple micro-contributions from different roles on the knowledge collaboration platform can be finished at affordable transaction costs and a la ow trust environment.
Domain Experts (DEs)
Domain Experts are tasked with inspecting and organizing data in the KGs. When under heavy monitoring mechanism, Domain Experts possess the authority to upload knowledge graph data. While executing this, they also benefit by supplying premium knowledge graph data, and parties ready to join as Domain Experts must first be appointed or nominated by existing Domain Experts.
These interested parties are required to win the support of the EpiK community by bagging votes and every vote is equivalent to the lockup of a single EPK Token. Those without the required amount of votes will be eliminated as Domain Experts. Domain Experts can also be voted out and punished in the event of uploading fake or junk data.
Note that generating knowledge graph data is no easy task and Domain Experts cannot on their own, complete them independently. Hence, Bounty Hunters.
Knowledge Node (KNs)
The role of knowledge nodes is to provide storage and bandwidth for knowledge graph data. They also gain lots of benefits by supplying adequate data storage and data access services. Therefore, the more data that is stored, the bigger the revenue, and the higher the data download traffic downloaded, the higher the revenue.
The trusted governance of Epik Protocol is supported by Decentralized Autonomous Organization Technology. This helps to manage organizations in a decentralized manner with the use of codes to ensure that rules are adhered to, and also to enforce immutability for altering data through blockchain technology. All issues in an organization that needs governance can always be dissolved by DAO. In this case, verified individuals will have to vote to decide results.
The trusted finance mechanism at Epik Protocol is gotten from DeFi Technology which is just perfect for today’s crypto world in which the majority of users hold mainstream gems such as Bitcoin and Ethereum. For users like this to enjoy the Epik platform, it would be necessary to have a lending service to facilitate this. A user would first have to convert their BTC to eBTC through Epik Protocol’s cross-chain gateway. They would then need to stake their eBTC to borrow EPK tokens using Epik’s lending service. Finally, such users can select their route to participate in Epik’s ecosystem after receiving their EPK tokens.
Under the technical architecture of Epik Protocol, the underlying storage is an intricate construction of knowledge graphs that need a lot of micro-collaboration thus the minute bin-log files will be generated in the process of collaboration and stored on the customized Filecoin Layer 2.
Epik Protocol’s Core Components are structured atop underlying storages and are comprised of three things:
- Consensus Mechanism
- Virtual Machine
- Ledger On-Chain
What the Consensus Mechanism does is to follow Proof-of-Spacetime, Proof-of-Storage, and Proof-of-Replication from Filecoin. Epik Protocol makes use of a unified 8M sector size to manage the multiple small files in its protocol, thus creating a chance for a huge amount of low-level node machines which were not able to participate.
In terms of Smart contracts, Epik Protocol encodes on-chain incentive rule and this is supported by Filecoin’s Actor contract model. This incentive rule is for each participant in the ecosystem. With the EVM contract model, Epik Protocol migrates financial services and governance services from the Ethereum ecosystem to the knowledge graph collaboration ecosystem.
In the Epik ecosystem, the units of knowledge graph data and knowledge gateway are bin-log files that are smaller than 8M. Also, every bin-log file consists of a string of ordered operations that contain updates to the schema of knowledge graph data as well as n-triple data of every domain.
Epik Protocol is a huge fan of the open-source knowledge movement and anyone is allowed to become a domain expert according to the rules. They can also contribute to the knowledge graph data and gain benefits.
How EpiK Ecosystem is Constructed
Epik protocol is built around two main groups which are the storage group and the Knowledge group. The epic protocol focuses on smaller capacity node machines that are incapable of providing adequate storage for the FIL.
The major storage is catered for by collaboration with mining partners with small capacity node machines whose performance are inadequate to participate in the storage of FIL to build an EPK storage service which includes machine hosting, cloud computing, computer power tokens, cloud node machines, and storage pools while supplements the ecosystem.
Epik protocols knowledge group undertakes an entire process from schema definition of knowledge graph data. There is a high demand for force knowledge graph data by experts from various fields of knowledge who have systematic knowledge structure examples include Professors, opinion leaders, and popular writers.
Epik protocol will regularly recruit domain experts to increase the diversity of demanded knowledge graphs and also breaking them down into more subdivisions.
Knowledge Graph production will mainly be done by domain experts. There will be a collaboration between Epik protocol and various crowdsourcing platforms and professional annotation teams to help domain experts distribute manual tasks and cooperate with data annotation training institutions, to expand the number of bounty hunters available.
Epik knowledge graph data implies interesting applications but as the dataset improves in quality and quantity it will evolve to more useful applications. Epik data also aims to collaborate with various data analysis competition platforms to source more value of on-chain knowledge graph data.
Use Case scenarios of Epik Protocol
The on-chain knowledge graphs are the first step in cognitive intelligent applications. Due to its efficiency in data processing and knowledge processing inference, there will be an upgrade in existing AI products which will provide more effective solutions, potentially even as new form products.
Knowledge graph for Health Care
The health care industry is flooded with massive amounts of data with heterogeneous sources and it is limited to data with strong professionalism and a complex structure and this makes data fusion difficult.
Knowledge graphs will play a very important role for patients and medical practitioners in the healthcare industry.
An efficient knowledge graph integrating and delivering method which serves to ease the shortage problem of medical resources which is as a result of the numerous amount of medical smart application which came due to digitalization of medical resource, expansion of biomedical knowledge and proliferation of historical data.
The most common applications are:
- Smart medical guidance for patients which accurately matches symptoms with the departments and doctors, so to prove experience and ensure balanced allocation of medical resources.
- Intelligent medical auxiliary diagnosis for doctors this integrates medical information and historical data, which optimizes treatment plan and saves cost.
- Question answering system assists in market expansion for pharmaceutical corporates, which will answer frequently asked a question and gather data to refine user portraits, making it possible for relocation of labor resources and increasing customer conversion rate.
Knowledge Graph for education
This consists of a combination of knowledge graphs and machine learning algorithms which lead to the creation of intelligent adaptive learning for supporting the upper-level intelligent application.
Knowledge graph taps into the educational field by constructing knowledge systems bases on modularize curriculum, managing teaching resourcing and tracking individual learner’s pace, which will deliver a representation of the interaction between educators and learners.
Knowledge Graph for City Governance
Knowledge graph isolated data aggregates scattered on various government departments and various fields of production and life will realize the exchange of multi-source data. And for what purpose? To conduct in-depth storage of governmental affairs data and social data.
There is a need for huge stock of governance and defined development in modern cities. And as the data source of urban public management expand from government Al data to traffic, video, environment, other urban operational perception data and corporate data. KGs in this scenario, deliver intelligent applications to facilitate public security.
Knowledge Graph for public Security
Knowledge graph plays an important role in solving problems of data correlation band as well as data value storage which will empower intelligent clue analysis and case early warning.
With the advent of cross-departmental collaboration and integration, knowledge graph extracts entities such as people, things places, institutions, and virtual identities through data analysis, text semantics, and other methods.
Knowledge graph promises the evolution of public security intelligence research and judgment. It also efficiently assist the public security’s prevention and control and as a result, achieve accurate crime prediction and early warning in the future.
Knowledge Graph for general manufacturing
The general manufacturing industry has a large number of datasheets and complex knowledge structures. The Knowledge graph technology classifies and models basic manufacturing data to achieve multi-faceted coordination of the entire manufacturing process.
Through knowledge classification and modeling of basic data there is an integration of quantitative knowledge and event knowledge through knowledge extraction, mining complex relationships between entities which builds a manufacturing knowledge service platform.
Knowledge Graph for smart construction
The knowledge graph for the construction engineering industry is built on BIM data and specifications, as the three-dimensional model carrier with rich semantic information.
BIM is the digital expression of a physical building. Knowledge graph technology brings innovative ways to coordinate the entire process in the construction industry which are labor-intensive with dynamic and complex industry structures. This will enhance resource management capabilities, production efficiency, and product quality.
Knowledge Graph for intelligent risk control
The risk control process in the financial industry is reshaped using a combination of knowledge graphs and machine learning. The rapid growth of financial data in recent years has rendered the traditional method of risk control inadequate, while risk control is based on algorithms and knowledge graphs that the inefficiencies of the traditional method of risk control.
Knowledge Graph for a smart investment advisor
Collection and integration of data in the financial sector is a difficult task in the investment research field. With traditional analysts collecting information through different channels, it is difficult and time-consuming to integrate and model scattered data.
Financial data have the characteristics of extremely high timeliness and the high turnover rate of the industry desires the consistency of the report to be guaranteed, the knowledge graph solves this problem by reducing the cost of data collection and in turn, improves the efficiency of investment research.
Without a doubt, Epik Protocol is just the place for you if you have plans to boost the open-source knowledge crusade along with the Epik community. Epik Protocol is also the place for you if you wish to translate human knowledge into a knowledge graph thus contributing to humanity’s efforts towards AI development.