Semantic Graph Analysis to Combat Cryptocurrency Misinformation on the Web
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Authors
Kazenoff, Daniel
Seneviratne, Oshani
McGuinness, Deborah L.
Issue Date
2020
Type
Article
Language
Keywords
Alternative Title
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.
Description
Full Citation
Daniel Kazenoff, Oshani Seneviratne, Deborah L. McGuinness: Semantic Graph Analysis to Combat Cryptocurrency Misinformation on the Web. Advances in Semantics and Linked Data (ASLD) 2020: 168-176 http://ceur-ws.org/Vol-2722/
Publisher
CEUR-WS