Attribution tags form the foundation of modern cryptoasset forensics. However, inconsistent or incorrect tags can mislead investigations and even result in false accusations. To address this issue, we propose a novel computational method based on Large Language Models (LLMs) to link attribution tags with well-defined knowledge graph concepts. We implemented this method in an end-to-end pipeline and conducted experiments showing that our approach outperforms baseline methods by up to 37.4% in F1-score across three publicly available attribution tag datasets. By integrating concept filtering and blocking procedures, we generate candidate sets containing five knowledge graph entities, achieving a recall of 93% without the need for labeled data. Additionally, we demonstrate that local LLM models can achieve F1-scores of 90%, comparable to remote models which achieve 94%. We also analyze the cost-performance trade-offs of various LLMs and prompt templates, showing that selecting the most cost-effective configuration can reduce costs by 90%, with only a 1% decrease in performance. Our method not only enhances attribution tag quality but also serves as a blueprint for fostering more reliable forensic evidence.
@article{arxiv.2502.10453,
title = {Linking Cryptoasset Attribution Tags to Knowledge Graph Entities: An LLM-based Approach},
author = {Régnier Avice and Bernhard Haslhofer and Zhidong Li and Jianlong Zhou},
journal= {arXiv preprint arXiv:2502.10453},
year = {2025}
}
Comments
Accepted at Financial Cryptography and Data Security 2025 Conference (FC2025)