Semantic Graph Analysis to Combat Cryptocurrency Misinformation on the Web

Abstract

With the hype around blockchain technologies, misinformation on ‘get rich quick’ scams are becoming rampant. In this work, we describe a solution that puts in the groundwork to identify fraudulent users and track them across multiple blockchains using semantic modeling. The application of Semantic Web and Linked Data technologies provides a well-grounded solution to connecting fragmented but conceptually linked resources. This paper focuses on showing that through the integration of ontology-driven knowledge graphs and a queryable graph database, a novel off-chain protocol utilizing comprehensive cross-chain integration techniques can be used to link an identity across multiple blockchains, and provide a significantly enhanced foundation for provenance data analysis for scam activity detection. This foundation could help reduce the challenges users face as they try to safely and effectively navigate the decentralized cryptocurrency financial ecosystem.