ASGM-KG: Unveiling Alluvial Gold Mining Through Knowledge Graphs

TL;DR

This paper introduces ASGM-KG, a knowledge graph that consolidates information on artisanal and small-scale gold mining and its environmental impacts, using large language models and expert validation to improve knowledge representation.

Contribution

The paper presents a novel knowledge graph for ASGM that integrates data from diverse sources and validates it through expert review and automated frameworks, enhancing understanding of environmental crises.

Findings

Knowledge graph contains 1,899 triples about ASGM practices and impacts.

Automated validation achieves over 90% accuracy on the knowledge graph.

Framework performs as well as five baseline methods on a public knowledge graph.

Abstract

Artisanal and Small-Scale Gold Mining (ASGM) is a low-cost yet highly destructive mining practice, leading to environmental disasters across the world's tropical watersheds. The topic of ASGM spans multiple domains of research and information, including natural and social systems, and knowledge is often atomized across a diversity of media and documents. We therefore introduce a knowledge graph (ASGM-KG) that consolidates and provides crucial information about ASGM practices and their environmental effects. The current version of ASGM-KG consists of 1,899 triples extracted using a large language model (LLM) from documents and reports published by both non-governmental and governmental organizations. These documents were carefully selected by a group of tropical ecologists with expertise in ASGM. This knowledge graph was validated using two methods. First, a small team of ASGM experts reviewed and labeled triples as factual or non-factual. Second, we devised and applied an automated factual reduction framework that relies on a search engine and an LLM for labeling triples. Our framework performs as well as five baselines on a publicly available knowledge graph and achieves over 90 accuracy on our ASGM-KG validated by domain experts. ASGM-KG demonstrates an advancement in knowledge aggregation and representation for complex, interdisciplinary environmental crises such as ASGM.