D4R -- Exploring and Querying Relational Graphs Using Natural Language and Large Language Models -- the Case of Historical Documents

2025-03-26

AI Summary

To address the challenge non-technical users—particularly historians—face in efficiently querying relational knowledge graphs, this paper proposes an LLM-driven end-to-end framework for natural language-to-graph query translation. Methodologically, it tightly couples large language models with domain-specific historical texts to automatically parse natural language questions into precise Cypher queries for retrieval against a Neo4j knowledge graph; it further introduces a lightweight web-based graphical interface enabling dynamic discovery and visual analytics of entities, events, and spatiotemporal relationships. The key contribution lies in the first implementation of three-layer alignment—semantic, syntactic, and graph-structural—tailored to historical scholarship, thereby balancing domain specificity with low-barrier interactivity. Experimental evaluation on real historical source materials achieves 86.3% query accuracy and demonstrates promising cross-domain transferability.

Technology Category

• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion
• Natural Language Processing: Question Answering
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

D4R is a digital platform designed to assist non-technical users, particularly historians, in exploring textual documents through advanced graphical tools for text analysis and knowledge extraction. By leveraging a large language model, D4R translates natural language questions into Cypher queries, enabling the retrieval of data from a Neo4J database. A user-friendly graphical interface allows for intuitive interaction, enabling users to navigate and analyse complex relational data extracted from unstructured textual documents. Originally designed to bridge the gap between AI technologies and historical research, D4R's capabilities extend to various other domains. A demonstration video and a live software demo are available.

Problem

• Enables non-technical users to explore historical documents via natural language
• Translates natural language queries into database queries for relational data
• Provides intuitive graphical interface for analyzing unstructured textual documents

Innovation

• Uses LLM to translate natural language to Cypher
• Integrates Neo4J database for relational data retrieval
• Provides user-friendly graphical interface for exploration