On October 17, 2024, at the Decentralized AI Summit held at MIT, Origin Trail was recognized as the best decentralized AI project.

A knowledge graph is a technology that organizes knowledge in a relational structure, representing the relationships and context between various entities. It not only stores data and information but also transforms them into “knowledge,” emphasizing connections and semantic information.

You can think of a knowledge graph as a kind of knowledge database, but unlike regular databases, it focuses more on relationships and semantics. One of the most commonly encountered examples of a knowledge graph is the Google Knowledge Graph, which was developed by Google to improve the quality of its search engine. Even if users aren’t aware of its existence, it likely powers the results behind their Google searches.

In the decentralized world, the most well-known “decentralized” knowledge graph is Wikipedia. As an open-source, collaborative platform, Wikipedia operates in a distributed manner. However, Wikipedia does not use blockchain technology. Origin Trail, on the other hand, brings decentralized knowledge graphs onto the blockchain, enabling seamless integration with decentralized AI to offer transparent, traceable, and verifiable knowledge.

NeuroWeb, created by Origin Trail, is a parachain in the Polkadot ecosystem. Its on-chain activity is experiencing rapid growth. According to on-chain data from The Block, Polkadot parachains have been consistently breaking records for transaction activity, with NeuroWeb accounting for 70% of the transaction volume (as of October).

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Origin Trail, launched in 2017, is an established blockchain project. But how has it found relevance in the red-hot field of AI? This article introduces what Origin Trail is, its relationship with NeuroWeb, the concept of knowledge mining, the project’s future potential, its significant activity on Polkadot parachains, and its associated tokens, $TRAC and $NEURO.

Real-World Applications of Decentralized Knowledge Graphs

If the concept of decentralized knowledge graphs feels abstract or hard to envision, consider this real-world example involving a partnership with railway companies:

https://x.com/BranaRakic/status/1831225441467629734

A recent train derailment in Europe was caused by a wheel failure that wasn’t detected in advance. By integrating decentralized knowledge graphs with blockchain technology, data from multiple information sources can be consolidated and handed over to AI for real-time anomaly detection, potentially preventing such accidents.

Why does decentralization have an advantage in this kind of application? Because it is more secure, resilient, and trustless.

The same approach can also be applied in fields such as aerospace, automotive, defense, construction, and more, offering significant potential for future use cases.

Origin Trail Overview

Introduction to Origin Trail’s Decentralized Knowledge Graph (DKG)

Origin Trail is a longstanding project with a broad architecture and ecosystem, making it challenging to summarize succinctly. Let’s break it down step by step.

Development Journey of Origin Trail

Origin Trail initially served as a supply chain solution, using blockchain technology to improve supply chain management issues. This was the main direction proposed in the first version of its whitepaper. Later, it was discovered that more data, including real-world assets, NFTs, and others, could be integrated, shifting toward Web3 and launching its own Polkadot parachain, Origin Trail Parachain.

Origin Trail’s corporate partners include the British Standards Institution (BSI), Swiss Federal Railways (SBB), GS1 (the international barcode organization), and the Supplier Compliance Audit Network (SCAN), which includes Costco, Walmart, Target, and other well-known retailers. Applications have included import inspections for goods, whiskey ingredients, pharmaceuticals, agricultural products, and meat tracing, and even railway component anomaly detection, mainly in Europe.

Relevant cases can be viewed here.

With the development of AI, Origin Trail’s decentralized knowledge graph has connected more data and information. In 2024, the third version of the whitepaper was released, aiming to become a verifiable knowledge network for AI. This can be understood as a decentralized knowledge layer for AI, providing traceable and verifiable knowledge.

In October 2024, at the AI Summit held at MIT, participants included prominent companies such as Dell, Intel, and Nvidia. Origin Trail was voted by attendees as the Best AI Project.

Main Products of Origin Trail

Knowledge Assets

Each knowledge asset is represented as an NFT, which can be tracked and verified using blockchain.

Knowledge Asset Browser: DKG Explorer

Allows users to search for all knowledge assets within the database.

LLM-powered Chatbot: ChatDKG

A chatbot that answers questions based solely on knowledge contained within the DKG. Currently, its scope is limited to specific domains within the knowledge graph, so it cannot answer most everyday questions.

Twitter Bot: ChatDKG

Users can issue commands by tagging the bot. For example:

/ask @ChatDKG Why is Origin Trail the best AI project?

(The command can be modified based on the question.)

Note: The chatbot’s responses are limited to information contained within the Origin Trail knowledge graph.

Origin Trail Technical Architecture

Excluding the application layer, Origin Trail adopts a dual-layer architecture, consisting of the NeuroWeb blockchain as the first layer and the Decentralized Knowledge Network as the second layer.

The Decentralized Knowledge Graph (DKG) operates as a decentralized node network that functions off-chain. It integrates with multiple blockchains, such as Gnosis, NeuroWeb, and Base.

What is Knowledge in DKG? Knowledge encompasses data, information, semantics, and relationships. This knowledge is primarily stored in the DKG node network (off-chain). An encrypted hash of the data is uploaded to the blockchain, generating a unique Universal Asset Locator (UAL). A UAL acts as a blockchain-based hyperlink, with each UAL corresponding to a specific set of knowledge.

The DKG is an open, permissionless, decentralized node network. Anyone can become a node by staking $TRAC tokens. Knowledge within the network can be designated as public (shared across all nodes) or private (hosted by specific nodes).

Node Staking: Becoming a node requires staking $TRAC tokens.

Knowledge Transactions: Adding knowledge to the network involves paying $TRAC tokens.

Two-Layer Architecture Simplified:

• Knowledge Layer (DKG):
  • The decentralized node network where knowledge is stored and managed.
  • Supports integration with multiple blockchains.
  • $TRAC serves as the primary token for this layer, used for generating and managing knowledge and for staking nodes.
• Blockchain Layer (NeuroWeb):
  • A Polkadot-supported Layer 1 blockchain.
  • Facilitates operations such as encrypted proofs and hash generation for knowledge.
  • Uses the $NEURO token for knowledge mining rewards, gas fees, and governance.

What is Knowledge Mining?

Knowledge mining incentivizes contributions to the DKG by rewarding users with $NEURO tokens. While adding knowledge to the DKG requires $TRAC, contributors are rewarded with $NEURO to enrich the network, similar to incentivizing Wikipedia entries.

Four Steps of Knowledge Mining:

1. Discovery: Identify knowledge gaps with potential value.
2. Publishing: Share useful knowledge to bridge these gaps.
3. Enrichment: Improve and apply the knowledge (e.g., references, usage frequency).
4. Validation: The system evaluates the quality and confirms the knowledge.

Currently, knowledge mining requires some programming skills. For more information, refer to the official documentation. Future developments may include user-friendly activities, such as mining through the @ChatDKG Twitter bot. Stay updated by following the official Twitter account.

$TRAC and $NEURO Tokens: Supply and Purchasing

How to buy $TRAC

The total supply of $TRAC is 500 million tokens, with 400 million (80%) currently in circulation. These tokens are distributed across five chains: Ethereum, Gonsis, Polygon, Base, and NeuroWeb. Contract addresses can be found [here].

You can purchase $TRAC on the following exchanges or directly on the above-mentioned chains:

How to Purchase $NEURO

The total supply of $NEURO is 1 billion tokens. Initially, only half of the tokens are released, while the remaining half will be gradually unlocked through block rewards. Currently, the circulating supply is 548 million tokens, primarily circulating on the Base, NeuroWeb, and Moonbeam chains. \
Officially recommended purchasing channels include:

• MEXC / Gate.io exchanges
• Stellaswap DEX on Moonbeam

Summary: OriginTrail as a Decentralized Knowledge Layer for AI

The rapid development of AI in recent years has been remarkable, but it also raises concerns about the risks of centralized AI. Fortunately, many decentralized AI projects are under development, some of which have been previously introduced by CryptoWesearch.

OriginTrail, as a decentralized knowledge graph, provides traceable, verifiable knowledge integrated with relational and semantic context for AI. This addresses issues of data source transparency in centralized AI systems, positioning OriginTrail as a collaborative project for AI applications.

While the whitepaper has gone through three versions, this reflects an iterative approach rather than frequent directional changes. The project began with on-chain supply chain data, extended to a wider range of on-chain assets, and further evolved into a collaborative knowledge layer for AI.

Throughout AI’s development, discussions about data privacy, data source transparency, inference process transparency, and verifiability have persisted. A decentralized knowledge graph can address these issues. OriginTrail, rooted in supply chain solutions, has accumulated a wealth of on-chain knowledge in specific fields over the years. Combined with its extensive network of enterprise collaborations, its future development is highly promising.

Disclaimer:

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