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This paper introduces a document analysis system for managing historical digital libraries that enhances conversational retrieval through on-the-fly knowledge modeling. By employing a graph-based structure to store facts from both expert archivists and document retrieval processes, the system allows for complex queries that leverage long-term dependencies and expert knowledge integration. The key finding is that this approach significantly improves the richness and comprehensiveness of information generated from historical records, surpassing traditional extractive methods.
This system transforms how we interact with historical records, enabling complex queries that weave together expert knowledge and document retrieval in real-time.
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.
