Blockchain-based knowledge graph for high-impact scientific collaboration networks

Yao Yao, Meghana Kshirsagar, Gauri Vaidya, Junying Liu, Yongliang Zhang, Conor Ryan

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

In this book chapter, we introduce a framework leveraging the intelligence of the crowd to improve the quality, credibility, inclusiveness, long-term impact and adoption of research, particularly in the academic space. This integrated platform revolves around a central knowledge graph (KG) which interacts through artificial intelligence (AI) algorithms with the community. In combination with Internet of Things (IoT) technology and blockchain, a highly productive environment including liquid governance and arbitration is created to fairly acknowledge and attractively incentivize contributions of valuable intellectual property (IP) to this knowledge base. In the proposed platform, various stakeholders customize their terms of agreement to be followed while validating the transactions on blockchain, known as smart contracts. Through the interaction of smart contracts and stakeholders, the agreement based on objective (scientific) criteria will gradually emerge from the simulated interaction and, if applicable, its experimental/empirical verification.

Original languageEnglish
Title of host publicationBlockchain Technology for Secure Social Media Computing
PublisherInstitution of Engineering and Technology
Pages119-138
Number of pages20
ISBN (Electronic)9781839535444
ISBN (Print)9781839535437
Publication statusPublished - 1 Jan 2023

Keywords

  • Consortium blockchain
  • Knowledge graph
  • MultiChain
  • Scientific collaborations
  • Smart contracts
  • Social media

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