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OriginTrail is a decentralized infrastructure that builds and maintains a Decentralized Knowledge Graph (DKG), a global semantic data structure for linking and verifying Knowledge Assets in RDF format. The DKG operates on a permissionless peer-to-peer network of nodes and is integrated with multiple blockchains for discoverability, verifiability, and ownership of data. It functions as a verifiable knowledge layer that combines symbolic AI (structured, connected knowledge graphs) with neural AI (LLMs and generative models) to create neuro-symbolic AI systems with improved data provenance and reliability. Data is published as Knowledge Assets with Uniform Asset Locators (UALs), enabling AI agents and applications to query, reason over, and build upon distributed knowledge while preserving tamper-proof origin and ownership. The network includes Core and Edge Nodes for data hosting and exchange, secured through staking of the native TRAC token by delegators. It supports enterprise use cases such as supply chain traceability (e.g., real-time tracking for rail and construction), Industry 4.0 data exchange, life sciences, decentralized science, and ethical AI applications. The protocol is multi-chain and emphasizes human-centric AI by giving users control over their data and AI models. As of 2026, the DKG continues to host millions of knowledge assets and serves as infrastructure for verifiable data in AI and real-world applications.

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OriginTrail

OriginTrail creates a verifiable multi-chain knowledge graph (DKG) for AI data and real-world data, providing a trusted source.

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Tomaz Levak

Brana Rakic

Žiga Drev

OriginTrailTRAC

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