Semantic Information Modeling with Retrieval-Augmented Generation for Academic Digital Libraries

Information Resource Management (IRM) has become increasingly critical in academic digital libraries due to the rapid growth, heterogeneity, and complexity of scholarly data. Efficient organization, access, and use of academic information pose major challenges in modern digital knowledge infrastructures. The Traditional querying mechanism that was mostly rely on volumes of keyword type suggestions or metadata-driven indexing, often paying less attention to capturing the semantic meaning, contextual relationships, or the intentions of the users. This caused less skewed understanding toward exact context, reduced the retrieval success rate upon intricate queries, and left us with no adaptability for changing scholarly content. This work confronts these limitations by offering a novel formula for an integrated semantic information framework that could be embedded in a Retrieval-Augmented Generation (RAG) architecture. The framework is going all out to build transformer-based embeddings for constructing dense semantic representations, employ vector space-based similarity searches for scalable, efficient retrieval, and exploit large language model (LLM) for context-aware and well-informed response generation. A single pipeline is established to yield cohesive outcomes where metadata of teachers is utilized to formulate semantic vector indexing, which retrieves most related educationist and researcher articles and produces answers that embrace coherence and credibility. From the experimental results, the model achieved significant improvements in terms of retrieval relevance, context content accuracy, and organization efficacy over traditional keyword-based and metadata-driven retrieval systems.The above findings strongly indicate that semantic-RAG-based models have opened a new and efficient way for navigating the academic information space, which is greatly beneficial for the future development of digital libraries.