Introduction

OriginTrail is an ecosystem that builds trusted knowledge infrastructure for artificial intelligence. It aims to solve the disinformation challenge in the AI ​​era by promoting the discoverability of information and ensuring the source of information. OriginTrail is committed to building a sustainable global economy by organizing trusted, AI-ready knowledge assets. It is based on its unique decentralized knowledge graph (DKG), combining multiple blockchains to create value based on network effects. It leverages its unique decentralized knowledge graph and OriginTrail Parachain to provide AI-based search and solutions to businesses and individuals around the world. DKG enables you to:

• Easily discover, query and integrate knowledge from multiple sources

• Securely share semantic data (knowledge) between systems and decentralized applications

• Monetize APIs and data with exchangeable and non-exchangeable tokens (knowledge tokens)

• Easily build customized verifiable data pipelines

• Integrates with existing SSI and blockchain tools

OriginTrail has the support and partnerships of world-class organizations such as the British Standards Institution, SCAN, Polkadot, Parity, Walmart, World Federation of Hemophilia, Next Generation Internet of Oracle and the European Commission. These partnerships help drive the growth of OriginTrail’s trusted knowledge base in trillion-dollar industries while providing a verifiable knowledge network, particularly in driving the economics of RWAs.

DKG

OriginTrail Decentralized Knowledge Graph (DKG) is a global, open data structure composed of interconnected knowledge assets structured as an RDF-based knowledge graph. It is hosted on the open, permissionless OriginTrail Decentralized Network (ODN), which combines blockchain with knowledge graph technology. OriginTrail connects to multiple blockchains such as Ethereum, Polkadot (via NeuroWeb), Polygon, Gnosis, and more. It is powered by the TRAC token, which is used to manage relationships between DKG network participants. Running an OriginTrail node also makes you one of the contributors hosting DKG and eligible for TRAC token rewards. As a developer using OriginTrail DKG, you can create and maintain knowledge assets that can be used in Web3 applications. You can also use standardized technologies such as GS1 EPCIS, RDF/SPARQL, JSON-LD and other W3C and GS1 standards.Why combine blockchain with knowledge graphs? Blockchain and knowledge graph are two different types of networks:

-Blockchain is a network of trust. They run on decentralized stateful protocols, enabling verifiable shared state for applications such as decentralized identity, asset tokenization (NFTs), decentralized finance, trusted multi-party computation, and more. -Knowledge graph is a semantic network. When Google first came up with the term “knowledge graph,” they explained it as “things, not strings.” Knowledge graphs connect highly structured, machine-understandable semantic entities into a semantic data network to achieve powerful data functions such as search, reasoning, recommendations, advanced machine learning, etc. Knowledge Graphs inherit the technology stack concept of the Semantic Web (introduced as the “original” Web 3.0 by Tim Berners-Lee, the inventor of the WWW). These two technologies are brought together in the OriginTrail Decentralized Knowledge Graph to form the specific vision of Semantic Web3 - a user-owned, data-centric, trusted, and semantic Web.

System structure

The OriginTrail technology stack is engineered to bring real-world assets to Web3, enabling discoverability, verifiability and connectivity of physical and digital assets in a consistent Web3 data ecosystem. Two key requirements needed to implement this Web3 infrastructure are the ability to ensure trust through decentralized consensus and leverage semantic, verifiable asset data to represent complex real-world relationships and characteristics (such as ownership, location, and business background). These different requirements require the two different types of technologies mentioned above - blockchain and knowledge graphs. Blockchain is a network of trust designed to enable trusted computing through decentralized consensus, much like trusted computer processors around the world. Knowledge graphs, on the other hand, are semantic data networks. Powering systems such as Google, NASA, Amazon, and more, it is a connected graph data structure best suited for representing complex assets and their relationships in the real world. The OriginTrail technology stack takes full advantage of blockchain and knowledge graphs by incorporating them into two network layers.

In the following sections, we dive into the two technology layers and their interactions. We distinguish several sub-layers of the DKG layer (layer 2):

-ODNNetwork layer, consisting of a peer-to-peer network of DKG nodes hosted by individuals and organizations, implements S/Kademlia.

-ODNdata layer hosts knowledge graph data, distributed in separate instances of the graph database across the network.

-Service layer implements various core and extension services such as authentication, standard interfaces, and data pipelines.

-Consensus layer implements interfaces to multiple blockchains hosting trusted smart contracts and is used to manage relationships between nodes. It also implements trustless protocols (currently supporting Ethereum, xDai Blockchain, and OriginTrail NeuroWeb).

-Application layers, including Dapps and traditional applications, use OriginTrail DKG as part of their data flow.

Blockchain-OriginTrail multi-chain consensus layer

The OriginTrail consensus layer consists of a blockchain, providing the required trusted computation for DKG to operate in a decentralized environment. Currently deployed on Ethereum, Gnosis Chain, Polygon, and NeuroWeb, OriginTrail is a multi-chain system capable of integrating and connecting all native assets of the interconnected blockchains, extending through asset graphs and smart contract-based applications such as data markets. In the current version of DKG (an Ethereum-based blockchain), the OriginTrail ecosystem is essentially “outsourcing” the functionality of the first layer. With the addition of OriginTrail NeuroWeb, the blockchain layer is now an integral part of the OriginTrail ecosystem technology stack for the first time. OriginTrail NeuroWeb also introduces a native token, NEURO - a utility asset not only for blockchain gas fees and inclusive governance, but also provides a way to incentivize growth in DKG usage. Together with TRAC, these two tokens form the token economics of the OriginTrail ecosystem, driving network effects and creating value for all network participants. OriginTrail thus becomes an ecosystem with two technical components, two network layers, and two utility tokens. The two layers of OriginTrail technology - the DKG (layer 2) and the consensus layer (layer 1) - can greatly benefit from each other when tightly integrated. By using OriginTrail NeuroWeb as a blockchain custom-built for DKG, the OriginTrail technology stack gains additional functionality and significant improvements in scalability and performance.

Basic concepts in DKG

Knowledge Asset is a product of the OriginTrail Decentralized Knowledge Graph (DKG), combining various standardized components from the blockchain and knowledge graph domains. The core element of DKG is the “Knowledge Asset,” explained together with other important components below.

A Knowledge Asset is a container for valuable information that belongs to you, capable of describing any physical object, document, abstract concept, digital, or string. It exists within a decentralized knowledge graph, making it easy to discover while ensuring the integrity of the information and ownership of the Knowledge Asset through the underlying blockchain. More precisely, a Knowledge Asset is a web resource identified by a Unified Asset Locator (UAL), an extension of the traditional URL. It includes:

An asset graph containing Knowledge Asset data represented in RDF, stored on the DKG (not the blockchain).

Immutable proof and ownership records: Consisting of asset graph state proofs and ownership records represented by non-fungible tokens stored on the blockchain.

The asset graph consists of assertions, representing the state of asset content. Assertions are stored on the DKG and have encrypted fingerprints for verification. Each assertion can be independently verified for integrity by recalculating the encrypted fingerprint and comparing the result with the corresponding blockchain fingerprint record. Technically, an assertion is serialized using N-Quads (stored on the DKG) and utilizes an encrypted fingerprint (N-Quads graph Merkle root, immutably stored on the blockchain) for assertion verification. Knowledge Assets can contain both public and private data. Public assertion data is replicated to the OriginTrail Decentralized Network and made publicly available, while private assertion data is contained within the asset owner’s private domain (e.g., an OriginTrail Node operated by the asset owner, such as an individual or company). Overall, a Knowledge Asset is a combination of NFT records and semantic records. With the dkg.js SDK, you will be able to perform CRUD (Create, Read, Update, Transfer) operations on Knowledge Assets.

State finality

Similar to a distributed database, OriginTrail Decentralized Knowledge Graph applies replication mechanisms and requires mechanisms to achieve a consistent state of knowledge assets across the network. In OriginTrail DKG, state consistency is coordinated using a blockchain that hosts state proofs of knowledge assets, as well as replicated commit information from DKG nodes. This means that updates to existing knowledge assets are accepted by network nodes (similar to how nodes accept knowledge assets upon creation), and all accepted state operations can be used. There are three stages of knowledge asset status:

• LATEST: Indicates the status of Knowledge Asset to be updated, waiting for the submission of the DKG node. Once the commit is received, the status is transferred to LATEST_FINALIZED.

• LATEST_FINALIZED: The status of the latest submission accepted by the network.

• HISTORICAL: Any previously completed state, identifiable by its state hash. Resource Description Framework (RDF) is a W3C standardized model designed to represent data about physical objects and abstract concepts (resources). It is a model that expresses relationships between entities in a graphical format. RDF schema provides a mechanism for describing related resources and their relationships. It is similar to object-oriented programming languages, but it describes properties in the form of resource classes. RDF implements queries through the SPARQL query language. UALs (Universal Asset Locators) are identifiers owned by the DKG, similar to URLs in traditional web contexts. UAL follows the DID URL specification and is used to identify and locate specific Knowledge Assets in the OriginTrail Decentralized Knowledge Graph (DKG). UAL consists of 5 parts:

• did (decentralized identifier) predicate

• dkg (decentralized knowledge graph) predicate

• blockchain identifier (otp:2043 = OriginTrail NeuroWeb Mainnet)

• blockchain address (e.g., the address of the NFT smart contract associated with the relevant asset)

• contract-specific identifier, such as the ID of the NFT token

• query and fragment components.

For example, a UAL might look like this: didotp:2043/0x5cac41237127f94c2d21dae0b14bfefa99880630/318322. This UAL refers to the decentralized knowledge graph on the mainnet, with the blockchain address 0x5cac41237127f94c2d21dae0b14bfefa99880630, and the token ID 318322.

Token Economics

The token was issued in 2018 with a total supply of 500,000,000 tokens. Currently, 402,324,425 tokens are in circulation, representing an 80% circulation rate. The current token price is $0.83 USD, with the peak price reaching $3.863 on November 2, 2021, and the lowest point around $0.2. This marks a fourfold increase in value. Regarding token distribution, the majority of tokens, 50%, were allocated to investors, 18% to founders, 20% to project development, 5% to team advisors, and 5% to liquidity. This means that aside from the 50% allocated to investors, the remaining 50% is also held by their team, which raises some concerns about the distribution method.

Conclusion

In conclusion, this project can be considered as a project deeply involved in the knowledge graph aspect of blockchain. However, we are aware that in the Web2 era, there are many companies focused on knowledge graphs. While the concept is indeed innovative, the practical application value is currently limited, similar to the big data field. To truly realize the potential of knowledge graphs, it is necessary to achieve interoperability of data across industries and to effectively mine the value of data. This requires cumulative efforts and cannot be achieved overnight. However, with gradual accumulation, breakthroughs can occur suddenly, similar to ChatGPT.

Additionally, this project began in 2017 and has been continuously transitioning to the latest trends. Recently, it released whitepaper 3.0, signaling a shift towards AI from knowledge graphs, which seems reasonable. Moreover, it frequently announces collaborations with various B2B companies, indicating a decent project foundation and ongoing developments. The only concern lies in token distribution, which is somewhat centralized. However, after years of consolidation, this issue has been somewhat mitigated. As for token strategies, further discussions are underway within the community.

Disclaimer：

1. This article is reprinted from [书中自有大饼屋], Forward the Original Title‘去中心化知识图谱龙头-OriginTrail 项目分析’,All copyrights belong to the original author [Teacher Zhu321]. If there are objections to this reprint, please contact the Gate Learn team, and they will handle it promptly.
2. Liability Disclaimer: The views and opinions expressed in this article are solely those of the author and do not constitute any investment advice.
3. Translations of the article into other languages are done by the Gate Learn team. Unless mentioned, copying, distributing, or plagiarizing the translated articles is prohibited.