D4R -- Exploring and Querying Relational Graphs Using Natural Language and Large Language Models -- the Case of Historical Documents
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916663447781376 |
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| author | Boeglin, Michel Kahn, David Mothe, Josiane Ortiz, Diego Panzoli, David |
| author_facet | Boeglin, Michel Kahn, David Mothe, Josiane Ortiz, Diego Panzoli, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20914 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | D4R -- Exploring and Querying Relational Graphs Using Natural Language and Large Language Models -- the Case of Historical Documents Boeglin, Michel Kahn, David Mothe, Josiane Ortiz, Diego Panzoli, David Information Retrieval Artificial Intelligence Computation and Language Machine Learning H.3; H.3.3; I.2.7 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. |
| title | D4R -- Exploring and Querying Relational Graphs Using Natural Language and Large Language Models -- the Case of Historical Documents |
| topic | Information Retrieval Artificial Intelligence Computation and Language Machine Learning H.3; H.3.3; I.2.7 |
| url | https://arxiv.org/abs/2503.20914 |