Developing Relation Types of Cryptocurrency Anti-Money Laundering Knowledge Graph

Abstract

Anti-money laundering involving cryptocurrencies has become a popular research topic in recent years. Moreover, constructing a knowledge graph of cryptocurrency anti-money laundering in a small sample of judgments to prevent cryptocurrency money laundering has become an essential issue for an improved understanding of the relationship between crime patterns and emerging financial technologies. The research method of this study is that we conducted a named entity recognition task and identified the key relation types to construct a cryptocurrency anti-money laundering knowledge graph (KG). Accordingly, we developed the “Judicia17,” a key relation type for cryptocurrency anti-money laundering KG. The contribution of this study is that the proposed “Judica17” relation types of cryptocurrency anti-money laundering KG can be applied to construct a legal knowledge graph.