---
license: apache-2.0
task_categories:
- text-retrieval
- question-answering
tags:
- rag
- gdelt
- knowledge-graphs
- retrieval
pretty_name: GDELT RAG Source Documents
size_categories:
- n<1K
---

# GDELT RAG Source Documents

## Dataset Description

This dataset contains source documents extracted from the research paper "Talking to GDELT Through Knowledge Graphs"
(arXiv:2503.07584v3). The documents are used as the knowledge base for a Retrieval-Augmented Generation (RAG) system
focused on GDELT (Global Database of Events, Language, and Tone) analysis.

### Dataset Summary

- **Total Documents**: 38 pages
- **Source**: Research paper on GDELT Knowledge Graphs
- **Format**: PDF pages with extracted text and metadata
- **Use Case**: RAG knowledge base for GDELT-related queries

### Data Fields

- `page_content` (string): Extracted text content from the PDF page
- `metadata` (dict): Document metadata including:
  - `title`: Paper title
  - `author`: Paper authors
  - `page`: Page number
  - `total_pages`: Total pages in source document
  - `file_path`: Original file path
  - `format`: Document format (PDF)
  - `producer`, `creator`: PDF metadata
  - Other PDF metadata fields

### Data Splits

This dataset contains a single split with all 38 documents.

### Source Data

The source material is the research paper:
- **Title**: "Talking to GDELT Through Knowledge Graphs"
- **Authors**: Audun Myers, Max Vargas, Sinan G. Aksoy, Cliff Joslyn, Benjamin Wilson, Lee Burke, Tom Grimes
- **arXiv ID**: 2503.07584v3

### Licensing

This dataset is released under the Apache 2.0 license.

### Citation

If you use this dataset, please cite the original paper:

```
@article{myers2025talking,
  title={Talking to GDELT Through Knowledge Graphs},
  author={Myers, Audun and Vargas, Max and Aksoy, Sinan G and Joslyn, Cliff and Wilson, Benjamin and Burke, Lee and Grimes, Tom},
  journal={arXiv preprint arXiv:2503.07584},
  year={2025}
}
```

### Dataset Creation

This dataset was created as part of the AI Engineering Bootcamp Cohort 8 certification challenge project.