# Linking Cryptoasset Attribution Tags to Knowledge Graph Entities: An LLM-based Approach

arXiv: https://arxiv.org/abs/2502.10453 · HTML: https://mlsanity.com/paper/2502.10453/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 48 references that are in the map.
- First posted on arXiv 12 Feb 2025.

## Builds on (most cited first, 20 of 20)

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- [Spams meet Cryptocurrencies: Sextortion in the Bitcoin Ecosystem](https://mlsanity.com/paper/1908.01051/) (2019)
- [Watch Your Back: Identifying Cybercrime Financial Relationships in Bitcoin through Back-and-Forth Exploration](https://mlsanity.com/paper/2206.00375/) (2022)
- [Safeguarding the Evidential Value of Forensic Cryptocurrency Investigations](https://mlsanity.com/paper/1906.12221/) (2019)

## Abstract (arXiv)

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.