Semantic micro-contributions with decentralized nanopublication services

Area of Science:

• Computer Science
• Information Science

Background:

• Linked Data publication is complex and centralized.
• Existing methods struggle with subjective or original data entries.
• Decentralized approaches are needed to simplify data publishing.

Purpose of the Study:

• To present a novel approach for decentralized Linked Data publishing.
• To enable direct publishing of small Linked Data statements by users.
• To address the limitations of traditional, heavy-weight Linked Data release processes.

Main Methods:

• Utilizing nanopublications for small, verifiable data statements.
• Developing Nanobench, a user-friendly interface powered by semantic templates.
• Implementing a decentralized network of services using grlc API generator and Triple Pattern Fragments (TPF) with quad extension.

Main Results:

• Nanopublications are cryptographically verifiable and queryable via a decentralized network.
• The combination of grlc and TPF extension provides reliable and efficient querying.
• Nanobench significantly eases Linked Data publishing for inexperienced users.

Conclusions:

• The proposed approach simplifies Linked Data publishing through decentralization and user-friendly tools.
• Nanopublications and the Nanobench system offer a viable alternative to traditional methods.
• This facilitates broader participation in the Linked Data ecosystem.