D4R -- Exploring and Querying Relational Graphs Using Natural Language and Large Language Models -- the Case of Historical Documents
This addresses the challenge for historians and other non-technical users in accessing and analyzing unstructured textual documents using AI tools, though it is incremental as it applies existing methods to a new domain.
The researchers tackled the problem of enabling non-technical users, like historians, to explore textual documents by developing D4R, a platform that uses a large language model to translate natural language questions into Cypher queries for retrieving data from a Neo4J database, resulting in a user-friendly graphical interface for intuitive interaction with complex relational data.
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.