D4R -- Exploring and Querying Relational Graphs Using Natural Language and Large Language Models -- the Case of Historical Documents

Name: D4R -- Exploring and Querying Relational Graphs Using Natural Language and Large Language Models -- the Case of Historical Documents
Keywords: H.3, FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Computation and Language, Artificial Intelligence (cs.AI), H.3.3, Computer Science - Artificial Intelligence, I.2.7, H.3; H.3.3; I.2.7, Computation and Language (cs.CL)

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Jan 2025Embargo end date: 01 Jan 2025Publisher:arXiv

Authors: Boeglin, Michel; Kahn, David; Mothe, Josiane; Ortiz, Diego; Panzoli, David;

doi: 10.48550/arxiv.2503.20914

arXiv: http://arxiv.org/abs/2503.20914

D4R -- Exploring and Querying Relational Graphs Using Natural Language and Large Language Models -- the Case of Historical Documents

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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.

8 pages, 7 figures

Keywords

H.3, FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Computation and Language, Artificial Intelligence (cs.AI), H.3.3, Computer Science - Artificial Intelligence, I.2.7, H.3; H.3.3; I.2.7, Computation and Language (cs.CL), Information Retrieval (cs.IR), Computer Science - Information Retrieval, Machine Learning (cs.LG)

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