OriginTrail
  • Get started with OriginTrail
  • OriginTrail Ecosystem — Call for Papers (Coming Soon)
  • Key Concepts
    • Decentralized Knowledge Graph (DKG)
    • DKG key concepts
  • Build with DKG
    • Quickstart (test drive the DKG in 5 mins)
    • ChatDKG builder toolkit
      • DKG SDK
        • Development environment setup
        • DKG Javascript SDK (dkg.js)
          • Interact with DKG paranets
          • Knowledge submission & curation
          • Paranet's incentives pool implementation
        • DKG Python SDK (dkg.py)
      • DKG paranets
        • Deploying a DKG paranet
        • Building with DKG paranets
        • Syncing a DKG Paranet
        • Initial Paranet Offerings (IPOs)
          • IPO specification
          • Launching your IPO
          • Paranet's incentives pool
          • IPO voting
      • AI agents
        • ElizaOS DKG agent
        • Custom DKG Python agent
        • Custom DKG JavaScript agent
    • DKG Edge Node
      • DKG Edge Node architecture
      • Get started with the Edge Node boilerplate
        • Automated setup with the installer
        • Manual setup
        • Usage example
      • Customize & build with the Edge Node
      • Knowledge Mining and dRAG examples
      • DKG Edge Node inception program
      • DKG Edge Node API documentation
    • DKG Core Node
      • Run a V8 Core Node on testnet
        • Preparation for V8 DKG Core Node deployment
        • V8 DKG Core Node installation
      • Run a V8 Core Node on mainnet
        • Preparation for V8 DKG Core Node deployment
        • V8 DKG Core Node installation
  • Delegated staking
    • Delegated staking—Introduction
      • Step-by-step staking
      • Redelegating stake
  • Integrated Blockchains
    • Base blockchain
      • Connect to Base
    • Gnosis chain
      • Connect to Gnosis
    • NeuroWeb
    • Teleport instructions - NeuroWeb
    • Bridging to Moonbeam
    • Deployed smart contracts
  • Bounties & rewards
    • General bug bounty
    • Code contributions & V8 bug bounty
  • Whitepapers & RFCs
    • OriginTrail whitepaper
    • OriginTrail RFCs
  • Useful Resources
    • What's new with OriginTrail V8
    • DKG V8 guidebook
      • Protocol updates
      • Feature roadmap
      • How to upgrade to V8?
    • Public nodes
    • Tutorials
    • Test token faucet
    • Development principles
    • Community created resources
    • Linked data & knowledge graphs
    • Available networks, network details and RPCs
    • OT Node Engine implementation details
      • Modules
      • Command Executor
    • Contribution guidelines
      • Guidelines for automated test contributions
    • Explore the OriginTrail ecosystem
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  • Why combine blockchain with knowledge graphs?
  • System architecture

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  1. Key Concepts

Decentralized Knowledge Graph (DKG)

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OriginTrail Decentralized Knowledge Graph (DKG) presents a global, open data structure comprised of interlinked Knowledge Assets structured in an RDF knowledge graph hosted on an open, permissionless decentralized network of DKG nodes. It's designed to support the verifiable Internet for AI based on an open, permissionless knowledge economy in which knowledge is the primary asset class.

As a builder, you can create collaborative knowledge graphs using DKG paranets, incentivize the growth of your knowledge graph with Initial Paranet Offerings (IPOs), with a combination of public and private (and hybrid) , and use them within an ecosystem of composable services on multiple blockchains.

In hopes of a better understanding of DKG, the analogy can be compared to traditional centralized SQL solutions like so.

DKG
SQL Server

Paranet

Database

Knowledge collection

Table

Knowledge asset

Individual record (row)

Triplets

Values (columns)

OriginTrail DKG strongly focuses on interoperability through standards such as semantic web standards (RDF, SPARQL, JSON-LD, etc.), Verifiable Credentials and Decentralized Identifiers (DIDs) by W3C, as well as GS1 standards such as the Digital Link and EPCIS 2.0 for real-world asset tracking (and to which OriginTrail core developers have actively contributed to).

OriginTrail DKG is multi-chain, integrating with Ethereum and Polkadot ecosystem blockchains such as Gnosis, NeuroWeb, Base, and others. It is fueled by the TRAC utility token, which is used to manage relations between DKG network participants.

There are many ways to participate, such as:

  • Building dapps with DKG SDKs

  • Launching DKG paranets

  • Publishing knowledge to the DKG via knowledge mining

  • Delegating TRAC tokens to DKG nodes to help secure the network and earn TRAC

  • Running DKG Core Nodes, helping to host the DKG network, and earning TRAC node operator fees

  • Open-source contributions to DKG code

  • Aharing your ideas and joining the discussion in Discord, Telegram, Reddit, X

Why combine blockchain with knowledge graphs?

Blockchains and knowledge graphs are two different types of networks:

  • Blockchains are trust networks. They run on decentralized stateful protocols enabling a verifiable shared state and are used for applications such as decentralized identity, asset tokenization (NFTs), decentralized finance, trusted multi-party computation, etc.

  • Knowledge graphs are semantic networks. When Google first coined the term "knowledge graph", they explained it as "things, not strings". Knowledge graphs connect highly structured, machine-understandable semantic entities into one semantic data network. This enables powerful data capabilities, such as search, reasoning, inference, recommendations, and other forms of symbolic AI. Knowledge graphs inherit the technology stack idea of the Semantic Web (introduced as the "original" Web 3.0 by Sir Tim Berners-Lee, the inventor of WWW). They are particularly well positioned for use with LLMs as they provide highly contextualized knowledge (or annotated data)

If you want to jump into the code, head over to the ChatDKG builder toolkit page.

System architecture

OriginTrail synergizes blockchains and knowledge graphs in a layered architecture.

Blockchains are trust networks established to enable reliable computation through decentralized consensus, operating as a global, dependable computer. In contrast, knowledge graphs serve as semantic data networks for knowledge management. The OriginTrail architecture is crafted to ensure a trustworthy environment for AI applications by leveraging these two fundamental layers.

The following sections of the documentation dive deep into each of the two technical layers and their interplay.

We distinguish several sub-layers of the DKG layer (Layer 2):

  • Data layer, hosting the knowledge graph data, distributed across the network in separate instances of graph databases.

  • Service layer, implementing various core & extended services, such as authentication, standard interfaces, and data pipelines.

  • Consensus layer, implementing interfaces to several blockchains hosting trusted smart contracts, used to manage relations between the nodes and implement trustless protocols (currently supporting Ethereum, xDai blockchain, and the OriginTrail NeuroWeb).

  • Application layer, encompassing both dapps and traditional applications which utilize the OriginTrail DKG as part of their data flows.

We also distinguish between:

  • Public, replicated knowledge graph, shared by all network nodes according to the protocol.

  • Private graphs, hosted separately by each of the networked nodes, connected with the public knowledge graph.

The public knowledge graph enables data discoverability by hosting a decentralized index of information replicated across the network, enabling search queries through its discovery protocols. Once information is discovered in separate (private) graphs, data exchange protocols are used to obtain full query results. An example of a data exchange protocol is a data marketplace protocol, implementing trusted data-for-tokens exchange.

The protocol actors are:

  • Data creator nodes (DC), responsible for publishing datasets to the DKG

  • Data holder nodes (DH), hosting the DKG datasets, incentivized by tokens deposited by DCs

  • Data viewers (DV), usually services or dapps that query the DKG

  • Data providers (DP), which provide data to DC nodes for publishing

The distinction between DC and DH nodes is only behavioral, as they implement the same interfaces (each node can be both a DH and a DC node at the same time).

Therefore, a dataset published to the DKG by a DC node:

  • Contains a cryptographic identity (DID) of the DC and DP, rooted in one of the supported blockchain networks

  • Is structured as graph-linked data

  • Has a corresponding set of cryptographic fingerprints (graph merkle roots) stored immutably on a blockchain

  • Is timestamped and has a "data lifespan" on the network

  • Is randomly replicated across peers based on a DKG content addressing scheme

In this way, any given graph vertex or edge (triple/chunk) in the DKG can be verifiably associated with a publisher DID, its originating dataset, and cryptographic hashes proving it is contained in that dataset, as well as enabling data integrity verification on-chain and off-chain.

The three layers of OriginTrail
OriginTrail conceptual architecture
Knowledge Assets