Fujitsu/ForQA_JP_Knowledge_Dataset

# ForQA_JP_Knowledge_Dataset

## Dataset Summary
- This dataset is a structured knowledge graph constructed using Fujitsu Knowledge Graph enhanced RAG for Q&A 2.0, one of Fujitsu's proprietary technology.
- `Fujitsu-ShareholdersMTG-FY2023.pdf` (Fujitsu Group Business Report FY2023 (Provisional)*) was used as the input document and converted into a knowledge graph.
- Because it is provided in Neo4j format, it can be easily visualized and can be utilized for simple chat applications based on the knowledge graph, as well as for research and development of AI technologies.

## Repository Overview
| Name | Description |
| ----------- | ----------- |
| `forqa2_ja.graphml` | Knowledge Graph for Q&A 2.0 constructed from `Fujitsu-ShareholdersMTG-FY2023.pdf` |
| `Fujitsu-ShareholdersMTG-FY2023.pdf` | Fujitsu Group Business Report FY2023 (Provisional)* |

* Note: The title provided is a provisional name for analysis purposes and may differ from the official document title.

## How to Use the Data
Demo code for using this data is available at:
https://github.com/FujitsuResearch/Knowledge_Data/

## Publication
Taku Fukui and Satoshi Munakata, "画像を含む文書から検索用洞察を生成することによるマルチモーダルRAGシステムの検索精度の改善", In Proceedings of the 39th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI2025), 4Q1-GS-10-01, 2025.
Kenichirou Narita and Satoshi Munakata, "Chunk-Link: Context-aware chunk completion", In Proceedings of the 2nd Workshop on Retrieval-Augmented Generation over Knowledge Graphs (RAGE-KG 2025), CEUR Workshop Proceedings, Vol-4079, short1, 2025.

## License
CC-BY-4.0

## Contacts
Kenichirou Narita: k.narita{at}fujitsu.com