Conversational Retrieval and On-the-Fly Knowledge Modeling of Historical Penitentiary Repression Records
For archivists and historians managing historical digital libraries, this work addresses the limitation of current RAG systems in holistic document interpretation and dynamic expert knowledge incorporation.
The paper presents a document analysis system for historical digital libraries that combines retrieval-augmented generation with on-the-fly knowledge modeling using a graph-based structure. The system enables complex queries across documents and integration of expert knowledge, generating richer information.
Recent developments in digital libraries increasingly favor conversational and natural language access to information through Retrieval-Augmented Generation (RAG). Although these approaches are effective for extractive tasks grounded in individual records, they remain limited in their ability to interpret document collections holistically and to incorporate expert knowledge dynamically. In this article, we present a document analysis system designed for the management of historical digital libraries that supports on-the-fly knowledge modeling. The system is equipped with the capability to store facts produced either by expert archivists or derived from document retrieval processes within a graph-based structure. Through continuous professional interaction, the system can retrieve information not only from primary sources such as documents, but also from previously modeled knowledge, with the graph-based index acting as a memory for the language model to access. This enables increasingly complex queries involving long-term dependencies across documents, link discovery, and the integration of expert knowledge that may not be explicitly present in the original sources. As a result, the proposed approach facilitates the generation of richer and more comprehensive information.