A Modular Framework for Knowledge Integration and Exploration in Digital Libraries

Abstract

Knowledge Graphs (KGs) offer a promising foundation for integrating heterogeneous resources, enabling complex querying and knowledge discovery. However, taking advantage of such knowledge infrastructures is often limited by the technical expertise required to construct, maintain, query, and explore them. Language Models (LMs) offer an opportunity to lower these technical barriers by providing natural-language interfaces and supporting the automation of otherwise complex knowledge management tasks. In this work, I present LibGraphLM, a modular language-model-based for framework combining domain-adaptable KG construction with human-usable exploration services. LibGraphLM assists in creating testbeds for investigating the interaction between KGs and LMs in the domain of digital libraries, which presents fundamental challenges such as sparse data, missing records, and heterogeneous resources. Preliminary modules address entity reconciliation and report creation based on the verbalization of connections between entities. Early results demonstrate the feasibility of these components, while highlighting open challenges related to evaluation, robustness, and generalization.